{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Support Vector Machine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Download the CIFAR10 datasets, and load it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have two ways to download the CIFAR10 datasets:\n",
    "- cd the path to './CIFAR10/datasets/', and run the 'get_datasets.sh'. Then it will automatically download the datasets and decompress it.\n",
    "- Enter the CIFAR10 website: http://www.cs.toronto.edu/~kriz/cifar.html and manually download the python version. Then put the datasets in local folder './CIFAR10/datasets/'.\n",
    "\n",
    "The CIFAR10 datasets named 'cifar-10-batches-py/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from CIFAR10.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the notebook\n",
    "# rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the CIFAR10 dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'CIFAR10/datasets/cifar-10-batches-py'\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show some CIFAR10 images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+yRR7XI65js2xOBpfGEQpmd7rqe/iOcqTfI98P7nWwc2zEUkHhoVhAYoUVKi0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S/+3Jwgvv3tbzu06+TkBF/+8pcBAFVVuetMp9OVURsAGPT62Fiz6oRbN5/DuxMbjYdBgFbbniaM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LykQbePTo/Wid1HB7jA+UrzYHW9CcuCx2E9QMb5i0NhkfVpfC9UWlADZtjZFIKNtfVmDVe2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R1VGpmWtUYbzIZ6fb81F1p9v1SZTVY1TWzK9zodqHH4za1cmhQRDwwQeuI/1v/OqvUQnd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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff0bc9a3358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "classes = ['plane', 'car', 'bird', 'cat', 'dear', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "num_each_class = 7\n",
    "\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, num_each_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + (y + 1)\n",
    "        plt.subplot(num_each_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subsample the data for more efficient code execution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape  (1000,)\n",
      "Test data shape:  (1000, 32, 32, 3)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train, val, and test sets\n",
    "num_train = 49000\n",
    "num_val = 1000\n",
    "num_test = 1000\n",
    "\n",
    "# Validation set\n",
    "mask = range(num_train, num_train + num_val)\n",
    "X_val = X_train[mask]\n",
    "y_val = y_train[mask]\n",
    "\n",
    "# Train set\n",
    "mask = range(num_train)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "# Test set\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reshape the images data into rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 3072)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Test data shape:  (1000, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: reshape the images data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Test data shape: ', X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subtract the mean images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/wB9ExOcZLce9XdLVwO3Avoi4HNjXfG9mp4mxyR8j/918e2bzL4AbgL3N9r3Ajb1EaGa9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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff0bc987ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Processing: subtract the mean images\n",
    "mean_image = np.mean(X_train, axis=0)\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.imshow(mean_image.reshape((32,32,3)).astype('uint8'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train -= mean_image\n",
    "X_val -= mean_image\n",
    "X_test -= mean_image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Append the bias dimension of ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 3073)\n",
      "Validation data shape:  (1000, 3073)\n",
      "Test data shape:  (1000, 3073)\n"
     ]
    }
   ],
   "source": [
    "# append the bias dimension of ones (i.e. bias trick)\n",
    "X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
    "X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Test data shape: ', X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Define a linear SVM classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class LinearSVM(object):\n",
    "    \"\"\" A subclass that uses the Multiclass SVM loss function \"\"\"\n",
    "    def __init__(self):\n",
    "        self.W = None\n",
    "    \n",
    "    def loss_naive(self, X, y, reg):\n",
    "        \"\"\"\n",
    "        Structured SVM loss function, naive implementation (with loops).\n",
    "        Inputs:\n",
    "        - X: A numpy array of shape (num_train, D) contain the training data\n",
    "          consisting of num_train samples each of dimension D\n",
    "        - y: A numpy array of shape (num_train,) contain the training labels,\n",
    "          where y[i] is the label of X[i]\n",
    "        - reg: float, regularization strength\n",
    "        Return:\n",
    "        - loss: the loss value between predict value and ground truth\n",
    "        - dW: gradient of W\n",
    "        \"\"\"\n",
    "        \n",
    "        # Initialize loss and dW\n",
    "        loss = 0.0\n",
    "        dW = np.zeros(self.W.shape)\n",
    "        \n",
    "        # Compute the loss and dW\n",
    "        num_train = X.shape[0]\n",
    "        num_classes = self.W.shape[1] \n",
    "        for i in range(num_train):\n",
    "            scores = np.dot(X[i], self.W)\n",
    "            for j in range(num_classes):\n",
    "                if j == y[i]:\n",
    "                    margin = 0\n",
    "                else:\n",
    "                    margin = scores[j] - scores[y[i]] + 1    # delta = 1\n",
    "                    if margin > 0:\n",
    "                        loss += margin\n",
    "                        dW[:,j] += X[i].T\n",
    "                        dW[:,y[i]] += -X[i].T\n",
    "        # Divided by num_train\n",
    "        loss /= num_train\n",
    "        dW /= num_train\n",
    "        \n",
    "        # Add regularization\n",
    "        loss += 0.5 * reg * np.sum(self.W * self.W)\n",
    "        dW += reg * self.W\n",
    "        \n",
    "        return loss, dW\n",
    "    def loss_vectorized(self, X, y, reg):\n",
    "        \"\"\"\n",
    "        Structured SVM loss function, naive implementation (with loops).\n",
    "        Inputs:\n",
    "        - X: A numpy array of shape (num_train, D) contain the training data\n",
    "          consisting of num_train samples each of dimension D\n",
    "        - y: A numpy array of shape (num_train,) contain the training labels,\n",
    "          where y[i] is the label of X[i]\n",
    "        - reg: (float) regularization strength\n",
    "        Outputs:\n",
    "        - loss: the loss value between predict value and ground truth\n",
    "        - dW: gradient of W\n",
    "        \"\"\"\n",
    "        \n",
    "         # Initialize loss and dW\n",
    "        loss = 0.0\n",
    "        dW = np.zeros(self.W.shape)\n",
    "        \n",
    "        # Compute the loss\n",
    "        num_train = X.shape[0]\n",
    "        scores = np.dot(X, self.W)\n",
    "        correct_score = scores[range(num_train), list(y)].reshape(-1, 1)    # delta = -1\n",
    "        margin = np.maximum(0, scores - correct_score + 1)\n",
    "        margin[range(num_train), list(y)] = 0\n",
    "        loss = np.sum(margin) / num_train + 0.5 * reg * np.sum(self.W * self.W)\n",
    "        \n",
    "        # Compute the dW\n",
    "        num_classes = self.W.shape[1]\n",
    "        mask = np.zeros((num_train, num_classes))\n",
    "        mask[margin > 0] = 1\n",
    "        mask[range(num_train), list(y)] = 0\n",
    "        mask[range(num_train), list(y)] = -np.sum(mask, axis=1)\n",
    "        dW = np.dot(X.T, mask)\n",
    "        dW = dW / num_train + reg * self.W\n",
    "        \n",
    "        return loss, dW\n",
    "    \n",
    "    def train(self, X, y, learning_rate = 1e-3, reg = 1e-5, num_iters = 100, \n",
    "             batch_size = 200, print_flag = False):\n",
    "        \"\"\"\n",
    "        Train linear SVM classifier using SGD\n",
    "        Inputs:\n",
    "        - X: A numpy array of shape (num_train, D) contain the training data\n",
    "          consisting of num_train samples each of dimension D\n",
    "        - y: A numpy array of shape (num_train,) contain the training labels,\n",
    "          where y[i] is the label of X[i], y[i] = c, 0 <= c <= C\n",
    "        - learning rate: (float) learning rate for optimization\n",
    "        - reg: (float) regularization strength\n",
    "        - num_iters: (integer) numbers of steps to take when optimization\n",
    "        - batch_size: (integer) number of training examples to use at each step\n",
    "        - print_flag: (boolean) If true, print the progress during optimization\n",
    "        Outputs:\n",
    "        - loss_history: A list containing the loss at each training iteration\n",
    "        \"\"\"\n",
    "        \n",
    "        loss_history = []\n",
    "        num_train = X.shape[0]\n",
    "        dim = X.shape[1]\n",
    "        num_classes = np.max(y) + 1\n",
    "        \n",
    "        # Initialize W\n",
    "        if self.W == None:\n",
    "            self.W = 0.001 * np.random.randn(dim, num_classes)\n",
    "        \n",
    "        # iteration and optimization\n",
    "        for t in range(num_iters):\n",
    "            idx_batch = np.random.choice(num_train, batch_size, replace=True)\n",
    "            X_batch = X[idx_batch]\n",
    "            y_batch = y[idx_batch]\n",
    "            loss, dW = self.loss_vectorized(X_batch, y_batch, reg)\n",
    "            loss_history.append(loss)\n",
    "            self.W += -learning_rate * dW\n",
    "            \n",
    "            if print_flag and t%100 == 0:\n",
    "                print('iteration %d / %d: loss %f' % (t, num_iters, loss))\n",
    "        \n",
    "        return loss_history\n",
    "    \n",
    "    def predict(self, X):\n",
    "        \"\"\"\n",
    "        Use the trained weights of linear SVM to predict data labels\n",
    "        Inputs:\n",
    "        - X: A numpy array of shape (num_train, D) contain the training data\n",
    "        Outputs:\n",
    "        - y_pred: A numpy array, predicted labels for the data in X\n",
    "        \"\"\"\n",
    "        \n",
    "        y_pred = np.zeros(X.shape[0])\n",
    "        scores = np.dot(X, self.W)\n",
    "        y_pred = np.argmax(scores, axis=1)\n",
    "        \n",
    "        return y_pred        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Gradient Check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define loss function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def loss_naive1(X, y, W, reg):\n",
    "    \"\"\"\n",
    "    Structured SVM loss function, naive implementation (with loops).\n",
    "    Inputs:\n",
    "    - X: A numpy array of shape (num_train, D) contain the training data\n",
    "    consisting of num_train samples each of dimension D\n",
    "    - y: A numpy array of shape (num_train,) contain the training labels,\n",
    "    where y[i] is the label of X[i]\n",
    "    - W: A numpy array of shape (D, C) contain the weights\n",
    "    - reg: float, regularization strength\n",
    "    Return:\n",
    "    - loss: the loss value between predict value and ground truth\n",
    "    - dW: gradient of W\n",
    "    \"\"\"\n",
    "        \n",
    "    # Initialize loss and dW\n",
    "    loss = 0.0\n",
    "    dW = np.zeros(W.shape)\n",
    "        \n",
    "    # Compute the loss and dW\n",
    "    num_train = X.shape[0]\n",
    "    num_classes = W.shape[1] \n",
    "    for i in range(num_train):\n",
    "        scores = np.dot(X[i], W)\n",
    "        for j in range(num_classes):\n",
    "            if j == y[i]:\n",
    "                margin = 0\n",
    "            else:\n",
    "                margin = scores[j] - scores[y[i]] + 1    # delta = 1\n",
    "                if margin > 0:\n",
    "                    loss += margin\n",
    "                    dW[:,j] += X[i].T\n",
    "                    dW[:,y[i]] += -X[i].T\n",
    "    # Divided by num_train\n",
    "    loss /= num_train\n",
    "    dW /= num_train\n",
    "        \n",
    "    # Add regularization\n",
    "    loss += 0.5 * reg * np.sum(W * W)\n",
    "    dW += reg * W\n",
    "    \n",
    "    return loss, dW\n",
    "\n",
    "def loss_vectorized1(X, y, W, reg):\n",
    "    \"\"\"\n",
    "    Structured SVM loss function, naive implementation (with loops).\n",
    "    Inputs:\n",
    "    - X: A numpy array of shape (num_train, D) contain the training data\n",
    "    consisting of num_train samples each of dimension D\n",
    "    - y: A numpy array of shape (num_train,) contain the training labels,\n",
    "    where y[i] is the label of X[i]\n",
    "    - W: A numpy array of shape (D, C) contain the weights\n",
    "    - reg: (float) regularization strength\n",
    "    Outputs:\n",
    "    - loss: the loss value between predict value and ground truth\n",
    "    - dW: gradient of W\n",
    "    \"\"\"\n",
    "        \n",
    "    # Initialize loss and dW\n",
    "    loss = 0.0\n",
    "    dW = np.zeros(W.shape)\n",
    "        \n",
    "    # Compute the loss\n",
    "    num_train = X.shape[0]\n",
    "    scores = np.dot(X, W)\n",
    "    correct_score = scores[range(num_train), list(y)].reshape(-1, 1)    # delta = -1\n",
    "    margin = np.maximum(0, scores - correct_score + 1)\n",
    "    margin[range(num_train), list(y)] = 0\n",
    "    loss = np.sum(margin) / num_train + 0.5 * reg * np.sum(W * W)\n",
    "        \n",
    "    # Compute the dW\n",
    "    num_classes = W.shape[1]\n",
    "    mask = np.zeros((num_train, num_classes))\n",
    "    mask[margin > 0] = 1\n",
    "    mask[range(num_train), list(y)] = 0\n",
    "    mask[range(num_train), list(y)] = -np.sum(mask, axis=1)\n",
    "    dW = np.dot(X.T, mask)\n",
    "    dW = dW / num_train + reg * W\n",
    "        \n",
    "    return loss, dW"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numerical: -8.059958 analytic: -8.059958, relative error: 6.130237e-11\n",
      "numerical: -7.522645 analytic: -7.522645, relative error: 3.601909e-11\n",
      "numerical: 14.561062 analytic: 14.561062, relative error: 1.571510e-11\n",
      "numerical: -0.636243 analytic: -0.636243, relative error: 7.796694e-10\n",
      "numerical: -11.414171 analytic: -11.414171, relative error: 1.604323e-11\n",
      "numerical: 12.628817 analytic: 12.628817, relative error: 1.141476e-11\n",
      "numerical: -9.642228 analytic: -9.642228, relative error: 2.188900e-11\n",
      "numerical: 9.577850 analytic: 9.577850, relative error: 6.228243e-11\n",
      "numerical: -5.397272 analytic: -5.397272, relative error: 4.498183e-11\n",
      "numerical: 12.226704 analytic: 12.226704, relative error: 5.457544e-11\n",
      "numerical: 14.054682 analytic: 14.054682, relative error: 2.879899e-12\n",
      "numerical: 0.444995 analytic: 0.444995, relative error: 4.021959e-10\n",
      "numerical: 0.838312 analytic: 0.838312, relative error: 6.444258e-10\n",
      "numerical: -1.160105 analytic: -1.160105, relative error: 5.096445e-10\n",
      "numerical: -3.007970 analytic: -3.007970, relative error: 2.017297e-10\n",
      "numerical: -2.135929 analytic: -2.135929, relative error: 2.708692e-10\n",
      "numerical: -16.032463 analytic: -16.032463, relative error: 1.920198e-11\n",
      "numerical: 5.949340 analytic: 5.949340, relative error: 2.138613e-11\n",
      "numerical: -2.278258 analytic: -2.278258, relative error: 6.415350e-11\n",
      "numerical: 8.316738 analytic: 8.316738, relative error: 1.901469e-11\n"
     ]
    }
   ],
   "source": [
    "from gradient_check import grad_check_sparse\n",
    "import time\n",
    "\n",
    "# generate a random SVM weight matrix of small numbers\n",
    "W = np.random.randn(3073, 10) * 0.0001\n",
    "\n",
    "# Without regularization\n",
    "loss, dW = loss_naive1(X_val, y_val, W, 0)\n",
    "f = lambda W: loss_naive1(X_val, y_val, W, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, dW)\n",
    "\n",
    "# With regularization\n",
    "loss, dW = loss_naive1(X_val, y_val, W, 5e1)\n",
    "f = lambda W: loss_naive1(X_val, y_val, W, 5e1)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, dW)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### loss_naive vs. loss_vectorized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss: 9.382264e+00 computed in 0.242533 seconds.\n",
      "Vectorized loss: 9.382264e+00 computed in 0.022621 seconds.\n",
      "Difference of loss: 0.000000\n",
      "Difference of dW: 0.000000\n"
     ]
    }
   ],
   "source": [
    "t_st = time.time()\n",
    "loss_naive, dW_naive = loss_naive1(X_val, y_val, W, 0.00005)\n",
    "t_ed = time.time()\n",
    "print('Naive loss: %e computed in %f seconds.' % (loss_naive, t_ed - t_st))\n",
    "t_st = time.time()\n",
    "loss_vectorized, dW_vectorized = loss_vectorized1(X_val, y_val, W, 0.00005)\n",
    "t_ed = time.time()\n",
    "print('Vectorized loss: %e computed in %f seconds.' % (loss_vectorized, t_ed - t_st))\n",
    "\n",
    "diff_loss = loss_naive - loss_vectorized\n",
    "diff_dW = np.linalg.norm(dW_naive - dW_vectorized, ord='fro')\n",
    "print('Difference of loss: %f' % diff_loss)\n",
    "print('Difference of dW: %f' % diff_dW)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Stochastic Gradient Descent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 2000: loss 403.810828\n",
      "iteration 100 / 2000: loss 239.004354\n",
      "iteration 200 / 2000: loss 145.934813\n",
      "iteration 300 / 2000: loss 90.564682\n",
      "iteration 400 / 2000: loss 56.126912\n",
      "iteration 500 / 2000: loss 36.482452\n",
      "iteration 600 / 2000: loss 23.327738\n",
      "iteration 700 / 2000: loss 15.934542\n",
      "iteration 800 / 2000: loss 11.508418\n",
      "iteration 900 / 2000: loss 8.614351\n",
      "iteration 1000 / 2000: loss 7.845596\n",
      "iteration 1100 / 2000: loss 6.068847\n",
      "iteration 1200 / 2000: loss 6.017030\n",
      "iteration 1300 / 2000: loss 5.407498\n",
      "iteration 1400 / 2000: loss 5.282425\n",
      "iteration 1500 / 2000: loss 5.760450\n",
      "iteration 1600 / 2000: loss 4.764250\n",
      "iteration 1700 / 2000: loss 5.395108\n",
      "iteration 1800 / 2000: loss 5.025213\n",
      "iteration 1900 / 2000: loss 4.858321\n"
     ]
    }
   ],
   "source": [
    "svm = LinearSVM()\n",
    "loss_history = svm.train(X_train, y_train, learning_rate = 1e-7, reg = 2.5e4, num_iters = 2000, \n",
    "             batch_size = 200, print_flag = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s47Zfbs46iiRJGiEL2gQ1ZVI5//nsOdy/voXe/oGs40iSpBGwoE1g5yxsoKt3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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff0b8aec0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the loss_history\n",
    "plt.plot(loss_history)\n",
    "plt.xlabel('Iteration number')\n",
    "plt.ylabel('Loss value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training correct 18789/49000: The accuracy is 0.383449\n",
      "Test correct 375/1000: The accuracy is 0.375000\n"
     ]
    }
   ],
   "source": [
    "# Use svm to predict\n",
    "# Training set\n",
    "y_pred = svm.predict(X_train)\n",
    "num_correct = np.sum(y_pred == y_train)\n",
    "accuracy = np.mean(y_pred == y_train)\n",
    "print('Training correct %d/%d: The accuracy is %f' % (num_correct, X_train.shape[0], accuracy))\n",
    "\n",
    "# Test set\n",
    "y_pred = svm.predict(X_test)\n",
    "num_correct = np.sum(y_pred == y_test)\n",
    "accuracy = np.mean(y_pred == y_test)\n",
    "print('Test correct %d/%d: The accuracy is %f' % (num_correct, X_test.shape[0], accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Validation and Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr: 1.400000e-07 reg: 8.000000e+03 train accuracy: 0.388633 val accuracy: 0.412000\n",
      "lr: 1.400000e-07 reg: 9.000000e+03 train accuracy: 0.394918 val accuracy: 0.396000\n",
      "lr: 1.400000e-07 reg: 1.000000e+04 train accuracy: 0.392388 val accuracy: 0.396000\n",
      "lr: 1.400000e-07 reg: 1.100000e+04 train accuracy: 0.388265 val accuracy: 0.379000\n",
      "lr: 1.400000e-07 reg: 1.800000e+04 train accuracy: 0.387408 val accuracy: 0.386000\n",
      "lr: 1.400000e-07 reg: 1.900000e+04 train accuracy: 0.381673 val accuracy: 0.372000\n",
      "lr: 1.400000e-07 reg: 2.000000e+04 train accuracy: 0.377531 val accuracy: 0.394000\n",
      "lr: 1.400000e-07 reg: 2.100000e+04 train accuracy: 0.372735 val accuracy: 0.370000\n",
      "lr: 1.500000e-07 reg: 8.000000e+03 train accuracy: 0.393837 val accuracy: 0.400000\n",
      "lr: 1.500000e-07 reg: 9.000000e+03 train accuracy: 0.393735 val accuracy: 0.382000\n",
      "lr: 1.500000e-07 reg: 1.000000e+04 train accuracy: 0.395735 val accuracy: 0.381000\n",
      "lr: 1.500000e-07 reg: 1.100000e+04 train accuracy: 0.396469 val accuracy: 0.398000\n",
      "lr: 1.500000e-07 reg: 1.800000e+04 train accuracy: 0.382694 val accuracy: 0.392000\n",
      "lr: 1.500000e-07 reg: 1.900000e+04 train accuracy: 0.382429 val accuracy: 0.395000\n",
      "lr: 1.500000e-07 reg: 2.000000e+04 train accuracy: 0.374265 val accuracy: 0.390000\n",
      "lr: 1.500000e-07 reg: 2.100000e+04 train accuracy: 0.378327 val accuracy: 0.377000\n",
      "lr: 1.600000e-07 reg: 8.000000e+03 train accuracy: 0.392551 val accuracy: 0.382000\n",
      "lr: 1.600000e-07 reg: 9.000000e+03 train accuracy: 0.391184 val accuracy: 0.378000\n",
      "lr: 1.600000e-07 reg: 1.000000e+04 train accuracy: 0.387939 val accuracy: 0.410000\n",
      "lr: 1.600000e-07 reg: 1.100000e+04 train accuracy: 0.388224 val accuracy: 0.389000\n",
      "lr: 1.600000e-07 reg: 1.800000e+04 train accuracy: 0.378102 val accuracy: 0.383000\n",
      "lr: 1.600000e-07 reg: 1.900000e+04 train accuracy: 0.380918 val accuracy: 0.383000\n",
      "lr: 1.600000e-07 reg: 2.000000e+04 train accuracy: 0.378224 val accuracy: 0.383000\n",
      "lr: 1.600000e-07 reg: 2.100000e+04 train accuracy: 0.376204 val accuracy: 0.380000\n",
      "Best validation accuracy during cross-validation:\n",
      "lr = 1.400000e-07, reg = 8.000000e+03, best_val = 0.412000\n"
     ]
    }
   ],
   "source": [
    "learning_rates = [1.4e-7, 1.5e-7, 1.6e-7]\n",
    "regularization_strengths = [8000.0, 9000.0, 10000.0, 11000.0, 18000.0, 19000.0, 20000.0, 21000.0]\n",
    "\n",
    "results = {}\n",
    "best_lr = None\n",
    "best_reg = None\n",
    "best_val = -1   # The highest validation accuracy that we have seen so far.\n",
    "best_svm = None # The LinearSVM object that achieved the highest validation rate.\n",
    "\n",
    "for lr in learning_rates:\n",
    "    for reg in regularization_strengths:\n",
    "        svm = LinearSVM()\n",
    "        loss_history = svm.train(X_train, y_train, learning_rate = lr, reg = reg, num_iters = 2000)\n",
    "        y_train_pred = svm.predict(X_train)\n",
    "        accuracy_train = np.mean(y_train_pred == y_train)\n",
    "        y_val_pred = svm.predict(X_val)\n",
    "        accuracy_val = np.mean(y_val_pred == y_val)\n",
    "        if accuracy_val > best_val:\n",
    "            best_lr = lr\n",
    "            best_reg = reg\n",
    "            best_val = accuracy_val\n",
    "            best_svm = svm\n",
    "        results[(lr, reg)] = accuracy_train, accuracy_val\n",
    "        print('lr: %e reg: %e train accuracy: %f val accuracy: %f' %\n",
    "              (lr, reg, results[(lr, reg)][0], results[(lr, reg)][1]))\n",
    "print('Best validation accuracy during cross-validation:\\nlr = %e, reg = %e, best_val = %f' %\n",
    "      (best_lr, best_reg, best_val))     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fAj7Vx/oXyLf8pJamT887gKOA3yTLrwQeAGZI+kxEXNPfjpL2It8D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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff0b96fc2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the cross-validation results\n",
    "import math\n",
    "\n",
    "x_scatter = [math.log10(x[0]) for x in results]\n",
    "y_scatter = [math.log10(x[1]) for x in results]\n",
    "\n",
    "# Plot training accuracy\n",
    "plt.figure(figsize=(10,10))\n",
    "make_size = 100\n",
    "colors = [results[x][0] for x in results]\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.scatter(x_scatter, y_scatter, make_size, c = colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('Training accuracy')\n",
    "\n",
    "# Plot validation accuracy\n",
    "colors = [results[x][1] for x in results]\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(x_scatter, y_scatter, make_size, c = colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('Validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test correct 369/1000: The accuracy is 0.369000\n"
     ]
    }
   ],
   "source": [
    "# Use the best svm to test\n",
    "y_test_pred = best_svm.predict(X_test)\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = np.mean(y_test_pred == y_test)\n",
    "print('Test correct %d/%d: The accuracy is %f' % (num_correct, X_test.shape[0], accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the weights for each class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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E7ftwqvX9Awhvaoo1ddz6NyquieP+M1NxxDd1g+Qyp5gHQc2Mi0gX5mzHBGtF\ncwZrcEofY6ernWdReD9zRUSkcAXP1MgMnoOxzbiiW0JM5/FF8Bzsr6SI7uVJnmtrtzEHx21gxFMW\n8HR8Bth68yTPIPcw2u5ML2vzFeN7Kn5YKPz7h6QzU1paWlpaWlpa85D+MqWlpaWlpaWlNQ9dV8x3\nc7np48b/RYWTQqE0p2XsvncZxjUyOkZKsz+Oc3smqkgzb0/DxeE/CpI58nlS3dMHTBiqlPSz9yAp\n6u2DOAmemSZ9eJ/n3GJyVR6wwZppChHaL+M+ilyMC+98CcjA5SHOA7qyl1T0TVauu3gN6GnrY6TE\nR7fwHfjuib9V8YV6XIgDJmfEvdtNBS0dJL/7SZPm93B22mU7zqn26VdUHJtN6t59P/hr7xGcTes+\nQ/rYVklbtV4Al6xahQvj7bdI+V71xxXYVwm+dT7NtYU8TLrYegXcIyLimQaeGykHpTWl3aji0Cgw\nyUQLOCD2HEjqcKhpLPubzvWbBA2sLgdpDCwmBX6+hnuLziJVHyTcZ0IxxQ0dqf58xlpe+2oVF6XQ\n9k3DVhX3DYNVfBIpQhrRbVHxvbcwT0+fpE+Sw3AqznrztzYbbVEYSMq/3xOkaB9jzm1oAsedzv71\nnPuJmCZd7xXD+V/b+nC01ebh2lxeztzxWoND661D4EKnrTgJW4bok+pBEFBCLtfX5Erb5YbiDHKx\ngwtjx1h3HKXpcMZmZJFVxcNNO1W8zwtEYqxlvYoows02mgcWCrHgLP7MMM7E4g62a8w6025WN7ZK\nDB0Bm/t9DaSS1fUxFV9KxvE14fH6nPvZ1czcDgplDe4fY4xU/QJ0NGHaOhB1GQznlsk2C58x5uNv\nOmByu3f/RsWZX2K9KPsyWzHC9oKRqu8ABY6VMPcBufNX0iGQf0kY99+6Hpzp2czzccEs8yUx+O9V\nPFPPWGswFSaOSTCtU6YirE4RYPeZfczNvHHm1mwGWxAaS0BqCfG4Aps/SfFTEZHwEtp1pAzc6OxC\nvLiA5/TIGIixeivPC/8uCvM6dfCes2/RXulp/O2Y8H1iLNp0Jmo1rk3nadbXDV1sCfog0pkpLS0t\nLS0tLa15SH+Z0tLS0tLS0tKah64r5mtPITXu7f2Eiu1juKymnUiNT06CEtw6vqFiv4THVHw6nMKA\nbib04BED2iruAFus2ULxwIAaXj+UCLaafZMUtfdqUtpvXSZ9KCKSMQauGOwhhez+OfBWwnFSohez\nSTne3wCb7bnMAAAgAElEQVQaejMJvHXFGeyx0IVU7OFs0pI3leOsKR7GiRLvDEoLNTnGXN7kTETh\nSKJ5qbyflPZyg+v32EWqN2uI1H2VHbwSkA4WSQukWKr15CMq3uhDgdQ3rKSbXVxBOyndOKGGXOlj\nEYqgjnyKNL/tDVMxSmNIzDobQlsHFvO+K0LAB5keINuS13E6TX8BR4oljPeJrwKBeE+DFfty6WOf\neNDekjdIpbeexcHTsxgEN7Ee96ojVdpBenuTqT9bK+nDkQGwQtKdpMZfNRXCvMXd5M4MBiuM3BCj\n4mBPijJ+7CJI4u0R3uf+Ivq8Ywt4NWchGOqVgSgV+76yZc79hCYyBs7cxNh7qosz+LZcYO60p+AM\ny1rOPQz1ggi9DzKWglJweSY3shXg9AbcZjefBE/ZAsFKdactKu7KATXeJ45RwjTjce9K1qisEfBZ\nuhv3VbEfR5ZbEqglfoo+jjiC46vfDVeUzFDA0jOdtXJwim0JeROgoDfewqWVdFOBim3hIMXwg8xT\nEZGKbf+m4rDj4MkSJ64pcjcYsq6be/DeTLv3NbN2LLHT2g+PgXKrT/CaC7dSqHPLBGPwpYXgojuf\nZm2tuQEHmyP1Vi6uuk93UrT3x1E4JqOd6OewNq7jsg8oNCWJtndaQB7l4Fu/VXGOK1sw3NOZv5nD\nPE8urwPhdZvO01s7xRoc1gLaPRj7zJz7yezM5zOiGUutUfS7u0Gx64qk7Sq2nGBuOgexXox0MMcL\nb8N1HyAmVG1nXYsdYHvQgM3CxYWxNl21s8Xjg0hnprS0tLS0tLS05iH9ZUpLS0tLS0tLax66rpiv\n2gYa8j1Den+bBWz3uB9nbC32BrF4bCe16H+R9GvpMOnktHRTIbJz4InlC0kfOl/mln3vAduUctSc\nOC3AkRbcgCNpwcrX5tzPQJ1FxVuXgl++U0bqe8jjVf7eCXTztZWk2f/6ypsqLrF/XsWBZTignMhi\nSmETRQzrkkCP/TbSuDku31Xx0VAKHeJfnJ/W9IPzYidxgFyMJg27/AToZCQXvBrjRyq5923SuTu3\n4Hh63c2i4kUx3OOFclBp7iwp5pcScBJ9tYkiks8WgHuiM0FZbzV8Yc79rL8K8om7DzTQ0gVKKn8N\n7DGYR/G8jjE++2Ov0RZuAzhvrm5k3OU8h7uleCnjtN/k3swQio2+2cz7uPrxGkcqrI3PuOgLLi6P\nZGxmhYHSunq4vkQbbtnGce4zow7XU1baj1U8OMLYvxwJOgsLAHm+6oKbc/HwnSo+/x2KCi5b8xMV\ndwbQfyIinXSVDL3EZ6y8lbUgZTEoubXK1Lej/BvTiGMsdJnO3PQaZpy7mVxvtudw0jXlgILDC0F+\nK+2459pHWV8cpUMD3EuqEy6vUjtbEZpCcPvenI2zuDOoQMU+Y1xbQS7bIKJMhVZbirjH6IPMlUt3\nWHnNSa4nMAX320GD+RsYB6ay3W/qPBGJePmnKh5ezBofegNIrvcJ3qvXlWfCTtMWj6PJzNkef8bv\n0QZWxZUhv1Kx6x6Qon86RRsXtjKe9grusr4Q1rLPiOMUd57isgcjaJvYFtMWl1DW41dGn1PxDW75\nKt57kfX4B76so56reJ5GBIAwbQfZijKYChabbWNeLwmjP8PjcLIWnuH10f13zLmf3gS2rPjPst3C\ntZJ1p6MeV3vUBZ7HF33o87jFYE7jPAhvXQfzt86FNcuy/6CK3aLYBuQ3yj27J7DtxLcBVPlBpDNT\nWlpaWlpaWlrzkP4ypaWlpaWlpaU1D11XzGfxIj04/kVSdPsrSLPmBZJKj7iCcyHoCPjPZsqM++82\nnR12gpRr0iZTUboGzssqWce5bcFP4WJYFksK3DMA9GIZJr3949a5RTvDEigaVvkr0oabg8Bw/XE4\nGCswu8g9jUdUXFNDejzJhptAgnCQeLhRcK8pjtT9mibS2yUluD5qP0fKNDOMdnGURo+DLPeu5DrH\n+/JVPLKEVHJSKOlZOW9KMe8ibd1egLNp+K63VdzRTbo108lUdLUONHBbF6ngbn+Y6Jphzs3zmb2i\n4rA465z7GbGC516ux3m2OxRs4xrEuJhKxAGV38M1dUVQAO/AKoqxJtTRH1ObGQg3OOP+Gurh/kvD\naZcbQ3E/Np3iGhzJEryiuZ/JJhxzDwXibD16kH7Ie4DUeGsvuCF+8Ocqdl0KGnnUylz5+3DQS+Eq\nUH70v4MMdu4+oOL+aRx4tmT6+dEoij5+4m5+LiJi1II91gzTfq1jJrfWcc61tPbiKE78HC6xsQbW\nI38n0EXdLIgwrpHXJ8SZXJj19KdzJqjmygjr3ZpLON0cpZu9QKGX6hlf+RtAchWFOJ5aTWeZPVHE\nfX1l014VJ6fzmukLzK/VszhWp+NxEY4eB8et3sj6YztrOoNPwEvWSubmSMjcs9xqnSii6zzGe4U9\nAxYejqNvPvU62yOaltykYs82PsNpGrwUbwGdV7aYzruLZDvCmV7mbOM65rJrETjao4itK448m++G\nWTDyk07gr2yWXXlzM3N2hQ/bGQbceVHAIhyyB6+y3rWMM17GclnLXDd9UcX9wWD6Lxczjl6v5Dlb\ncw/P1nUvc23d95ouVET2VYOYo2dBo2EXwP/L3Xivmk3MnfiUAhWveJnrto0ylpqqeP5OrALnNq+1\nqNjJguu6xxMEn32EZ1Pnhj9uS4XOTGlpaWlpaWlpzUP6y5SWlpaWlpaW1jx0XTFfmoVU8cWzuG8W\nuIDY3FwKeE0IhcKmGkEMgcG4FdwP41Doj6WYZXAHeGaZyRVYXUFqv/0+XF+r9vDzY6NgiOA0HH+f\nnZnbXMNnSFFWryc92BoOrnBNIFW8nsuWVxJwhHzt2RdV3Pi3FCgregZUkZjMPST6UqwsylQIdCjM\ndHbcFZx9V5NwHjlKNbkUgwt35ToDTYU6LZV8Vx8op7hdoeXrKo4q4eyo2Dt4T79zf63inGlQSKmF\ns72Gk0jzXu0m/e+8gKKbvndz5t6lfZz7GOlFKlhEJG4T6GKoEFwxUlWg4lF/Cm+uqiSVfrWccxm7\nKkjJf+8h0MBVfzDw6CDXPTUBMjg/w/gNOkN6etbkQnIaJMXuSOUWgrA7baCb4knaPvEe2sh68SEV\nLz+Dm3H0ln9S8fMvPK3i+DvAJ28dxHmzIBOcacvDSVV/FYQbFES6vWUd1/C5yV+quOdtE/4UkbEG\n2ngmmL8pNuH1FeuZI75xFN6MMRWYjchgTBbE0lfxx9hrEDJBX/WanKeTaaxxM638bUwy2xcmg8EZ\njlL4CK7pnmnWMr9+0GxED1hsOop1+QvuuG4P1TImlhdxLx5hYM3jteCVoDDW3JhVFhUPBOEK68+j\nfar9wXHTkzgK3dsoeiwikn0FJDlainO05wsUv83oNW0jWMI9F5sKBCeNggVnE3m2dB+/bHoN6/KW\nVYzTkz7cv+s5EJHrkmdVvNL1T/M4LVrE9o31MSbnrGFystfzTFifzXp88ggOO7/NbDnx8mAd8Z9i\n7XTt5D1nXUGB1WdBmI+x80X8DdrC87ucg9oWjgvW/VHWXRGR79zNnDp6iDX1ai5bVnquMFbDF7JG\nLDnP/b8YjjN3WwQoMCyAMzRbR3hP5xwQfO0BxsXiEPDi7Cx93nwR/CdzDYm/VzozpaWlpaWlpaU1\nD+kvU1paWlpaWlpa89B1xXwNp3HlJKSARpIiSOO7tuFWCEwDk9TVktILsOESKvLA9ZPkgnumxsd0\nbpsT7pm1zaSoPatAeK9n7lJx6oTpnJ+Bwyp2b5979lIK2XoZvROM4fckVobOH4FAimMp5mkcJS1b\nnY5jquJ1umS1F06iN7tJoeZM4IZoKgTz1d0PJrrtMli0bIyUvqN0+0LObWp8gfRpyDdwaRZ6075O\n1WCOjRdwGPmvAOdMHAYrJI89qWLbGGPCfwLXlfsUCOrBOlDC43m4I92rKdqZv5kUcfipued/9dWT\n6o4z/RPDddXNKs4tA1G8MYtdZ/c6zvYa8aYgZ8EEGCJrDLfNyirOqrq8FZfqqlrOe/NdwljpbQNP\neAlnWjpSHV0bVTyUDuYMmMVFeli4vnvGuY6S7dzz9CmQV9YaENDmJfTV6aP0yeRBUuym2pdSF1+g\n4mcHWCsWPg7CsLng8mrPBkmIiIS3kfY/sp5zPZcMgowGAjifza0SDH3F5Npt98KtFb8Ql9mZJYyl\nM8UgQveFpjPFBrim7fHMx6BjOMk8TWjMUXq6HoS1PAnkN36Vz3LZAc4YsIPC+6mNLLGfBy8X/BCs\n6ZUHHg33IZZ41qVDFcyJG9poQ5e1YLTkIdMZkN0XVHzzgn+Ycz+Fi/eo2LWYdda3nrHgbHK5PrqO\nbRZZVnCOTwSf0f8q8z04inXqQCI/N/ayliVO4XJsnOI8Ob8u1t+pIVMh4M+Kw+TTx/pdZcW16p7O\n/LKVs6a+fZJnmV82kyr1CZzisRsY493FuI4nd/LsG56iD/OmmGvRY8yVPabu3+SHW7Kxi3Vtdhlj\nUETEaz/tOuvGPAqd5llZMcGzPLyJZ4d3h2krzwbG5JvPWFScYXLqBU3zPWBwgK0dg9OMyZhs1rvG\nbH7+sX7mssin5P2kM1NaWlpaWlpaWvOQ/jKlpaWlpaWlpTUPXVfMN7YD50pUDe6IJ1/FefPxRM5h\nampbo+LkGFKDp6ykMVcsJV3pUUQK1G3tShUvNJ2FddkbZOJyjNSt32qKj9WdNp07dhfvs9MVF4KI\nyLl7cJ34xvO9dHY9KdFlz9+l4toe0qYpsRQSLQvBcbHklFXFXTtJOWafp4jlCblbxZ+8j1T80Ftg\npar8b6m40+Qwc5T2GTiAXO8mPRt0EtfHgRaw2M5lpKr7R0yusAtcf8o68NzaACwje+pJ7W8MJ519\nbNyi4rZaUrtRRxlndl/a5FI7LrXbwkGHIiKN7mC/aS/QU00j1x1i0AfrRh9UcVMBblTv2628ZzXF\nP4c9Ge+1q3Bs+o3QdvEduF5KBkEyMwmM2Ut+IDVHamAX6Cmpk/S27zDttPo04+iJIHBrZD3ofNtK\n7uE3briQXnkdN6N/tulcw4Oc/Vef3KfixmkKDMYN8ZqQDaCNl+xgNI9IkJGISJI37i7j+2AGj+1g\nptlu4lYbGD06BNdTTzXjYrwbRHjTFHyjssxUVHaYsZBdV6Dijjj6zXch43CgwfFO21WZrDNVQyCS\nJH8+68UexlR4C/2Uko6Dye3n3O8iG+P3TDNnJeZ24WoNXQqaqVoJUgsYxI11cYTtAaUTjCdLItfz\n+NhcN9+aYLaBNM+wJrrfSDvu6aAv1/Wwlr+2jPvfeYCxE76DMT56hJ+n1/NIDNzC3/YO8QwZ6WN8\nVPqxheTBUVyBIo5zaY6kgRW9Cnm2xI/wvGu8j/XYXgDOmjnM+PVOAoXuCeb5k7iKdqyr4d4W1dNG\nZ4PYojPuxXM5z53nXlUqTuv2Btbv9S5zi0bb2tnysCGBvynyxZnv3A0ytJVYVNwVxJaS1fu57v0e\nFAWOMDkYY/+ReT3wDDg3fdlXVPxaMWvthntwET79GG3BZoc/LJ2Z0tLS0tLS0tKah/SXKS0tLS0t\nLS2teei6Yr76DgqFGd64IPK3mVK3E0tVPOBPmi3E7wEVN71G+nGhyd11fAi3TWY5ac/WCVBFwUyL\nijclkIp2C+L1xhdIhwa8RFG2n/pyDSIiNxgU5DxSSSpz9wzIoXaYFLXLAhDAokCKo10JB/U0Z5Ou\ndO/Boea5FIdKyiCp8orHaMeMUNL1l/eCwxbFzS2a5gglxoFCPX/2/6l4/LanVLy5C0vLuTpw28Zm\n3BNhaSC/xlEKvXnVg3lik0jnls1yDlznOAUYJ7aCijPPkeZ2WmEqtFjP5wafmpuG780FMX2lj/Hy\ndjdOorZgXFu9ifRZbAau0+k2kwMkAGTS08Y4SE4lPd/WCwIZuZk0tOUgWKzC+l8qztrC/TtSgUVW\nFTdt5H5mapmD7nWmM6yGwaqnxkBAP1nAPPAeAk9EBIJhIi7isAlczc8vdpPCd21mXEf1gvLz3LhO\np17QWWX1XDff4CzrQvwncXQNz4Dnen/6HRU7b2F+VY1QkNS+iMKgUd0UGDxjsnx6+rKMHvAxucRm\n71FxrMsLXEMrqOZwMGfVMYvmp74BcHlQEWP+h1n030o/nFDRrcyjpe6M6xfW0sfpRWAbl0jupdDK\nujlVdFDFOS6Mm9dncS7bgsBiD10BLbfGmM73DOH6RUTcTtJnllT6tbCe69gaBsKsy+F5cmcHDsMu\nX7YLOJmwbmo8a5mMsPbPWnFZL7SADoPdQX7TDYzNQnfWKTzA81dcCfj0ajQY6q04Pjvsm6w7mwLB\nVq138UwcGKOApb2A50/R7RSEzjjN3/bdCY6zrGY9KnqQ+1/Ww/j1TWItS0gyWd2rWQdERPy9OVPT\n5sWYqTqOy9crGexrd2JdbB7kNSF+uDmXpvAZtWlc39ijjH+fZbRjtA/rxa400/rwC65zVRffRT6I\ndGZKS0tLS0tLS2se0l+mtLS0tLS0tLTmoeuK+bwugQ/CZknrWlJJj9ZUkYpbuIK09KoiHDmFi3BY\nuQeDvFK3kGIvH6Bw5rKIb6o4sQf3iV8QWKzCyrXFjoB8XG35Kh4N/s8599Np4fy41W7cz/ntYA+X\n75EqDSghFXkgF6db8FnSiZc3kjbPtvI+EdUUTJxdSbo6dhOpXhcvUrTpBunt8WqcGCJfEkeosPY2\nFW9wogil/QXO2Bq/i5TsspdN13MLKHOswarigHHSuaV2+n5zDSnfvVO0g0sA/Rdoojyp2fTx+XKT\naymClO+JpaA2ERH3EJw4z10ES7huBT3Vr6PdH/pH8M+ZSJL6bXVgiKidnPe4pAxs6VxJ35fOmsZH\nEEVap62k810CyT0PReJ2daScLaTofQ4xHmvXgsWjTfjaNRZU526ap3FPUVRvcjsopduOOyd6Del5\nax99NbGQ8bvmOFih3gXM80Q61xbXjmvNshZUISLi3QYqcjkHSo5dCg5alocrrd7NdH5fEEg6NhzU\nVTvKeEs4Cz4ObP4Br48EBQ/cyTl3xYXcT/IEg/UzfRQXFfkXcYSG99GX7ltAO7cEgML7amivWhfu\n3T6FM3FxGdjd2kPh1O21oNlTi8FISwPZTvDbac7du/Mtih03TdHH59eAFP1M2PhitelMNBFpiaLt\nbix5UsVJoxR89NgKRu9+ioPU+pdSdDntY7i8gveCy2czmY99qTh5Y/YwNm2eYLGyDuIZN5BtWNFc\nnOUo/TqUdTTRNB7XzdBOVfcw7y4abFPY+gLOuxNRr6l4hQ9uvqI3I1XcWAf+7ff4jYqtz9+o4gc+\nzjaKriM8u6WBbTyTMawVSa9yzSIi9jsPqbjCl9dNV/Ls6NvBMyXrJe5/0AkntFcseNZ4A6d9XgbP\nmrJtOPZXh7BlZ0+g6T6PM47Cchn/DfuZvx/kcD6dmdLS0tLS0tLSmof0lyktLS0tLS0trXlIf5nS\n0tLS0tLS0pqHruueqVWL2XOyx1S92mOMPREXEmC528+zJ6K+m9d7RfM+1Tb4aEwrVlt5BFY68CT7\nYdIjYcivNlN64I1ZqjI/bdq70jsFN8/25cBUEZGxZsoPlE7BqRsL2buVv4Lvqx0JsOlFx6ny2xXC\nvpxb3qTa8/SC1Sr286Cis2c593l4AE6duYz79HqVvSFVuznE1lFKTWafRWU1+w+GT7EfJuRl9ih1\nV3AvgZ/5soq9T1pU7O/NvqKMOBh9r9OjvGcPe8SiBtif1JoJez86w94Ft8v/pmL3tk+qeCKSPQYi\nIhEV93N9mdigZ4uxWZf3swdoaPHDKm5xoz+GveHyX21h/8z3QxlHCQfZb7PoVq7hYgHVivv72cMX\nl8Q88Gj605RGGA1gL9r9FexLejLOouKxQSpWe5ZR9Typmfnr7cHhwbPPsF8nz5M2OhXGnseDAczl\nqBbGS2sge3q21LKPsl7og8AWrqemg9eIiIz0sD8mKY/7meriM6q+wSHpPfXMwejXKANQ7ca4WuHE\nXo/uT7G3YvIl9s0ELWRJdf8qf9t1D/swO1pYs5ICvy2O1sob2D90qo0THGbbaa/pIPa93DTBwbVn\nphlfPbdyWG3cGOuJ/TXG6Ywn5WFCLrJ/9fZNrHW+Nvq+3Zu9c+N9tFuoB3uVPu3L+4iIlPSxfk2m\nseeqc4R72NLJvpyG7BdVnD5Cf1RMsjemKI2Dt9c2sYYu7ePefraNfV8ztaxHsW3sebPvtqq43Zl7\ndqTc+jhFY6ud9eXwLPNoIpq9tkMnGHelW9lr7FrFs+g5C2P8bhsV4GeCOfS4zXaTikM3sm+tqoe/\n9QilDEF8I+vxcBulEQqc1825H+ffsqYm7mKdizb4+Savz6m4z4u9SydMJZWcl1HGYqHpBIRXE2iX\n+Eb66ou9lLq4s5/9uf4jVF5vrWFex/k+LX+MdGZKS0tLS0tLS2se0l+mtLS0tLS0tLTmoeuK+aQP\nG23wIPZan3wqGS8vxxLe2Zuv4unt2PtvLMKOeTYem2qzGyk6yw+pbh46zMHATvup0P2pR0i9f/VV\nrmG1P2lmVydSrK+WkWIWEcn6FtbUssdAe5kDXGuikIo84Ybl/spO7t/pAOgiKJhU9JlSqsvGOINS\ngiI5TDg12IShynJVPDRG+nRZPzZ2RynaTvvWTy9Wsd9fgdh860CnEZN7VXxlL6UEgkOwvbavIlUb\n0QB6SImlvMHA09i+e5dy8HBcPWnbISfS7cH33qtiz72mA587scaKiNQFgdL83ThI292NdsxoJnaO\nAwXe1MM0GhmhwvP5XPCG5WdcX8UMNt6aLnDDQs5Flml/xoT7JDh6uY0UtiM10U8bP7IKZJYQAHYe\nmwF/J/ZQ3Xs0BGt5ayk3sSr0SRUX+jIGO2Z4jc8gaHdLIvNr7DlwvzUae3ugM9fpGkBZidgzYFQR\nkaZkygAEnwdRNLkzH4feJpYZEIUXxdAlp+4lFe+PBW2HHGGsztr5bO9GcNWS3XzucA9jPjKWsbfP\n6TEVf05ApPNRTwfXOfMmGObUQ4y7ZLGo+KUpq4ofXA1GeuqfKGlQspi+T1tOny01VTFofNl0eHs8\nfeO6+HUVb/KkfEJvLP+WP1IDvnHxZV0WEXEf5RSCctOpGLNHOVHitSiwrpOdddAWA24KaWNMhHkx\nl9sC6JvBGa7Vcpp76AzhQN7dcbzP/ja2CwQZPH8cqY2RYLVjpWwnyVnPdbTVgnaP3sI8yutiDS5P\npFxIagjz8ehzIHX7jaC9nDCqqnfMmk5w8OC5ZOthLvcn8oyKqWESBY6YFjYRWX4b+LTjDa6pIZJK\n5Meb2dYS7c62iLEIri9qEvx3cC/jqvFveMZn1IOt70nj2ZoQzsAdLeT1HqZD6M868bkfRDozpaWl\npaWlpaU1D+kvU1paWlpaWlpa89B1xXw1TaTAxxNBdXUXcdV42UAGjWtBL5PepNXLQkkherSQAvZf\nQro9v5404Z4yUvUum0npTnaS9l4vYKXxOv42LArEsNxzbrrS72nS3f8TAMJ7YgKE959LOMjxFjuv\nN9rACjFtoJ4DWTh9fPN4/XQ/33vHvEiTepmcYSvvxD1Vlgj+i2iZmzZ3hPbUkW5OzcUJ5mtYVFw4\nCRpwT6JNXQ+DZkc/h7syuJPh2NKAU7K1APTrkUTqPTPZdED0JO0f7svnRjoxPgYCGDcrQ8CyIiJ1\nXbRvbSvp7QcynlNxf6HJ9VEFAvFcRR+cv2xVcY4z6fauHYyD1SYce6kHt0nfEhxDCYVUT7ctA1N3\nmQ7MdaQm/HBMBYUy79xmONx307YCFTeXMmbbTf8mS+gAt3QlglsbTNXDVy7hNSNTzLuB/eCp3ntN\n2LWW1P7MSdBpxVq2DYz5MC5ERFZXMle7fHE0RUwwR2ayQDT+L+Hiig+gb18OB5MsjeJw7KoeqqSP\n5Zqcbq/T5y1ZYK+6HlBK9gKQUVgpn+sovdbEvWd9kXYMfJx5seiT4OLyfBD0iQscQvvvmzjV4cVg\nsHPXKP19Ypg+SNnEmhbkzZobMcaJB1WL2R7gfpY1/dZB5q8Y/FxEZMaHdeGVJp4P31qFe7loFPe2\nEcx6532JueOSwGf4OLN22LxAewsicZ695vaqipdN71RxRwj3nNcB2nq1hjnhSLVNsL56bcQtPRNA\nH1aeYfxvqQJhnvVkvKe04H5sC8lX8fZ7qeB/pBgU1ucEpr7Dk7auquJUhPJMTsIIqWOeVWUyJyZn\n3/X8GWEc1ibwHEmcZe70XwFDtvviwA13A8kNFYH81+/kPv1fYbw0bQQfxzTznkd6WP+NDPBsYCGn\njiS6/XHPTZ2Z0tLS0tLS0tKah/SXKS0tLS0tLS2teei6Yj63B3FWjPfhCBhqAgENRYNMRs+REo5I\nIY3pH4DbLsSJVF/5FQrUVSSSVo/tpNBjYTiF4kavkD4ciCHlf4MdV8GJYJxdASZkJCLiXggCeDAD\nl0leBm6dW18A+ckMjghLqMkGswtHw6LnSdEau0zuxyqQSWU8bdeYaVHxmiMgA8/lYJuLvRTKo2zl\n/JSZSrXJuj1c83iAVcWL3Wj3C0mkWxNMTrUu41cq7inkmqPiQHvPx5B6X2EhPR/VQcq/GjInERM4\nmA5Fg+aaFtJfk5UgXhGRB+pA0BuyuYfeDpDJ/mzQdGAfKXCnLMZpWjCo6uUWcNbqQ/Rf7Qo+y/so\n7xnlC1L27WTcGR0UeG1aNPfQUEeppKxGxXGrcbn1XyG931cCOvbfAI6evkDa3/fzFEA81UP/pJZS\n8PLqJPM93IqL1sWDgoHhL1OE0LsBR2HSbnDhhSrmQb03rlARkQ5XDkFNSAUlFLfR9rmDjMPSLSCt\nS3UUel3l9qCK/RrAJIH9rC8tnoyx+jjG+XZn7rN753+oOHIfLqzQUrYdOErOvsyRoirGfNgWXjPS\nx7hLXIg7uHmW+MVB+jJjEOTR78oamu4J2prtAl8Ol7IeRmUxOU9euV3F2TP/reKe1awVVVU4YkVE\nkrSKdqAAACAASURBVFyZU8t7+IwClk1xquea3FpOqngmirWv0vS36wYpNhrqDV6yXyC/sMGHsbKg\nlXF3sp1xbVtP0Vm/8j8N5uvu5TGdfgq39GwO2wWyfDkY2NsXbDtYz/N03Rh9W3KUe+6wMAbjR+mH\n0Dyej2/1g6zXRFLMdUkFbXp0Mxg8po2D5IcTaSMRkdZZsKVlmnU+Pe4WFT/vzt/PmIp6zwRSYDQ6\nmjHceQCsGHYzDr6uVp65XpdYU9K8uabWDHBpjOk5XncKt+gHkc5MaWlpaWlpaWnNQ/rLlJaWlpaW\nlpbWPHRdMV//C6SfH/Km6J/Ng7RsizvnmUWGk94dT+QssLcv4NzIHSAO66JYZpyN87heiubnHlEn\nVJyVC3YMPUdKu6UfR0NKIenAyVgKeomIuHuCBj7tTGHF1BEQyOlt4KAw0/lWM0U0fX3/f6o4QT6h\n4rqzFE8cyn1SxemeOG6G20EgF4fAZMsOgkKrg7geRykphP4wHb0lDR58rlMfaCsll5Tx+CApXMsl\n+rhvEiQROM69PNCN+6PFk3sZCOI9Q71I5wYttKh4g5W/PexOCjfZF0QkIuKcRrq6+CSuD694Utp5\ndYyjtxZz3dsugHzOT/DzvPtBcoHD/LxiEWn4NSPgg33+FOfzC8HRFOaNuyxz0iJ/CqX3gfBGR0j1\nT7jT3t2rwR7xFhxQkxXwo8AXmEe5EThmuqcZ+8ljvH9vIfgoYgdp9fY2rqcmBMfQgQgwx5YqirCO\nWecWYQ3xBFWW5oDt+4/g3CkJ4edV42CPrJv+QcUBlbgq26YZSyM+4N91E2CsylhTEcIKisrG9m5S\ncVcyWxOWHcbJLPJZcYT8gtha4OnKnApZz3Wee5x1bX0W2wmcopnMPSf2qfj1zWDnaAG79C6jHUI8\ncUJ9ZQ+Y9YgJlwWfAuvbc9iWEXfOhMvWzC3AWlfGWXszg6z3xeMmR+29uEJTCxk7HsVsDwkIB3kV\nbmD8LnXBLVr6E5B92HrW7qJo8G3WJeIeUwFa75sZ+45UTyJbEizpzLVjnQUqzk4G4XkepC1jb2GM\nny/jNeFBIMkmN+Zp7AzbXcJc2TbS4sy91dQwD4qG6fOFpoK9natox+RX5n7NGPEDk1ak3KnikOkn\nVRw4aHqWLeC9Slt5rg9NMtfOm9C2eytr5ycGeZa/lWcqomtnW8D2EdaE+iLWaWMMrP9BpDNTWlpa\nWlpaWlrzkP4ypaWlpaWlpaU1D11XzOfqgoPN2wXE1uZFYTWPFFKoQ0WkdOOcSUVnZZHSa3Rlh75T\nBejm9UxS1C7nSEvHCWns4aY7+FufR3nPWDBasw/F+Vb9DXhCRGTiYdL1s32kCt/Oonhdz5Xvq3iq\nCdfEdCBYwa/0OyoOXwxuas/BrZDZiKPN2sE1hWRyJlGQL4jNr4hUb8wCinw6SifKKB6XvAs3X28x\nRTirTI7FkP/GtZEzRPta14CRGjZz7zWBm3mfbrDgzkOgHdevgRIGXuR8wK6V9HdqKaldrz7wQXoS\n40ZE5OwAKe3dK/j7zlZS4Lbt4LyJg6S0G06ZsPACiiQ2xVEktCcAZ19GIUjqTQ/OGky8TGHPiRmc\nMdFh4L9kT4ohipgOkZunRrJw8+0cpC2eXQKi2VlAXxVdAp8snaW4bK2pQGZyMtftMchc6SkBDQSt\noW+rSsCfoc6M5bWB4NKEy6wDTZ0gUsvygjn3Uxb2BRWvHGBNufpVMKFl3wYVD86CPTzKaQvvJtBj\nUAyo0n2S+4+3My7e7Obelrjh2hspBUPPJNHnP4/KVzGzYn7y8QOvb2Wayr7XaIeotSCSsQiwWOaQ\nVcUu91JE9twLrEXTt4ELffYw9kP9aLfX0kBBXle499QbabeZfsbNqd2gvLjKz825Hzd/sM2mCda1\nzW2czXblCdx5rTaco2EruM++Y/ytPR5s2fwcjtXpHeDhi8HM8cx6Cg0fdspS8Zog3IJjgyAiR+rT\np0FsrRv/R8WDTqx5rXVsYZjOZO7kz+KMPJ7OeaIdLWwhSZvBFbhwmq8ELzQyf8OoWSpjzRTI/Gwf\nc/BXpSB7uyfXVuLMtYmIZEfwfKx3Z03e2/Wwir2WMc9/EI/jd2UTyHPFAN8DKj6Oyy/mAnGb289V\n7OfFOtL7PPfQuIObm/gY69fQ8bmu0veTzkxpaWlpaWlpac1D+suUlpaWlpaWltY8dF0x32wPqd/x\nbrBYWQip0thp3Acrmtn1n/Qi7r/azsdUHJwAYproYKf/uLloXx+p94FsCnQtrnpaxReHKLDnFcpr\nXP15z65Hn5xzP3Wrv6jiVXXgl4xa0snNfTtUvDqas6GeLSfVf4sn6Vd7CKnigZ+BFa5GUBgxYAEY\nxn6ctHTvLMUDXT8D2huiFqTDFDZE/xmhpFvr80gZb34FHOm8DcTZYwPtuPiZCpkWgILa4uETa90p\nwjm8DET2+AWw65pg2ry2Avz1dh344Os3g0p/dIFCjiIi93rS1oVp9I13B31TWwo+iY/mTL2lX+H+\na6pJYdvKOUcqdYHJbTaLezW4HrTlb3I9pWx4TcVGBffZt/uPKyT3QbWoBFfNK8G0xT2TuF6eXkhK\nPzqIORLRiEvKY4b0/jNP43b9nzTmcmEayOhMDX3u6kURzRorSLHfCXeaUc+4m93Gaxb7zf134XDV\n36v4tNynYmcXxtvLd72l4u0tXPdAGejq0hhIK+MqaN7H9Hlnu7mOBc6sNZX+vCYjG9xcPUUfJtvZ\n7uAouXWAMh+f4H5TtuB+TX+bcXfcC7dUkicopP8Yr/e7F1RjPwhqCTDhvC4TFpq8ajpjNB7c32kq\nItm6Ekdw6+FtKt7m9fKc+9lTQztWuIK/3YLAqEPCcyMtlXna78U9tPtTLHKmmufGgu+BM2ceA1XZ\nko6oOGUpeLLtMuOjZJL2iq3gfhypI85sNVjszRzc3Ayati7E/RY6RltecvonFcdd/KqKB0xFMRvj\ncKzu+Vva96snmGvFZndtxhMq7nUBQcbfTIHbKxWsiWHLWU9ERGp7cEtn1DAvLsayRoRdYN3ZNUHb\nH7PQbyN19P+Ox9gicqdpbfqS8FxPWczrczcwpoamwN9evwQv/iSL+xTZIe8nnZnS0tLS0tLS0pqH\n9JcpLS0tLS0tLa156LpivvDtpLfLTIURnf1Mhd9CSJv2VZPS/EEDyCD+Ts7wyWgmtTyKOUQ2CTvx\nPfpJS7c3UHjx9ep8FXuvJE0YZHLhBPu+qmJr9Gfm3M+qCpwINT0UG+1LAensGMDdddGLImA3fQqn\n4ugwaeMzr3D/QXach163cp91U5yLd3sfafxDVpyDlgDwiecw7ego3ZLH+z9bm6fiuD30a2sg5zkF\n95OefdYZt9y/DoLkRiykg5tyTGczfQfsZr+DtpryJ80dlML7f2aKVP1/5YEYbPtxG92xFOQqInJ5\nlPadqCP17OuJQzRpnOsbvh8n2KUfkEpuTf6eij/RiyPxkVJw7Kd8cNucWEb6POcoiOiCD3hiegHX\nneJFWzhSVdGmQonDtNneMVyPK66AaJLTaKOKFjhylgeTMM7kijywkVR98X7QWfpizuObaeOe3fyZ\n75MLKVQ4ksP9e3Ti8joXYDqoTUQ2hvBewzba+L4pXEy/fob+9LWBhtynf6ni1BjG9gl30HPUMNsI\nMmco+lgfhjMs/DLX2pcL2lywH2wz+DHQrqPU3Q2CzrOB25onuZ6OCfqpx+1r/Lwc9Gl3p61uukBR\n4tN3cs3n9tBuGc7ce6wT56N5vg0Kqg78iYq3V1DI9EQwfWldxfouIjIzgmsvVVgfu6pYWz2cWS+8\nvHA4v+1C0dJdAuavDKDvL56nXdw9QYGRQSD76k4wYncu7ZLujZOzqxcnmCPV58XaNnkQFN65gjnY\n2MHWj+WxOA+LrRQdPreIa13xLOg89DBj4cYvUtT6XD39dimQ+es09a8qDgnivFrPcRzLXgtMhWNb\n5661PUE8+ybSWBcXdZkKBMfiyNsaw3xxL2UOzmSxvthqQZJfdaLw6Fo/tr60huPwry1hHI23so0i\n7EbWvu0z4PIPIp2Z0tLS0tLS0tKah/SXKS0tLS0tLS2teei6Yr6KFtLP6VMgAz8vHC2VPuzET/4i\njjRLIUXZdtSDJJpGTQ6NSVLFTV04/noOm87/+TIp+VGDlHZwNynTiVnQQ1IYLonweNKYIiLVneAD\n1wUgzOBi3BEXwkg5e/qYzsk6jvNjbCPpV/8wvt8m5oLMZk6Rig6OAu01h5CWzPT/PPfgCdq0VuOW\ndJT2d9FP946Dp/57PanhESsOx4pxCqPdPMv1V0WSSi0sAc14t5F67b6ZOKKIVHrEMtq80JsifOfG\nOf/LL5oU9oVMPne2HwQpIjJbCVJdvtbk/iziMy59DKeH636uKSP3eRU7WcEbFZ2kqtfF4hg56k7B\nuPACUuCnfUjJ72yjX+sDcVtN/JR5IBgn563ZSMZIUj2FV4dccX1lbAN17D/HOHUjUy97W0ESA+E4\nLD2foJBiRhSp+qoRMNoNI7Tv5QDmaeBV5kGP6eywbRbw56VzcwsD9pTfruLkEBBFXyLYflcXhWSn\nVnL/Tw0z5++ZAQfleTGWXlzEWXATnYy3iUKQ5MrNzNmzzzAvnBab8EQp1yC7xSFyX0JRyaYAsM1s\nNuvV3rYCFWf7sFa87QmyjshgXW5pA5FN/wqcl7oFh2PwUZDPyI0WLigFh/KyZubNzCz9F3WGPhoa\nnvtYSjhgur5lxF7rwPxeZ0BvwwWMl4hdzLXuUSZMekeBiv3i6I9pV/rM6gvKDWj/iooX9rM+RLTR\nf9XfMFW2dKAChpkv4y2cKZiQka/i0IVg2AveOJWvXAZV3hoBXu+4la0G6RGsfVXHuf90Z7BbyVXa\nfWUKhTCvms4WvcsFbDvlyTOwZYxxJyLiYXJwTznTPwtmLCoecgNVXnyd18jdrNuLikCb3/Vj/KwI\n4jtBeTUu3aC94MKX+nj/B8KY40MdrC8FIfkqfkTeXzozpaWlpaWlpaU1D+kvU1paWlpaWlpa89B1\nxXz+Caaibq2k5W3xpPHcO3Bl9JRQTC3UlZTuLwTskeO3WsVjV3EVWdb+g4qdvUFtP7xMSu+LKezo\n7zRIP69NwcFVd5BUosskmFJEJPsYfMMjmdTy6Cw/H60jVRoQy3WPV+E+aAwiPTzpBUpxGbOqODYG\nZ1hdNW2x0BfnYWUBzhrPW8CInaYin46SXzBFROsvkzIN7KLtymykZDcE4JZsySXdHnkEZJswzjho\nsOIic06gTWZ7KMaY7g6aKWoH60ZBHsTdN1bFcUGkudtlLrJdZEoTvxkJwglKxrUY2so9j6b9h4qj\nL4HkGmJAtnv8QWSdruCAhADSzVZhHNxqciRdaCT1vikIXBgQM3cMOkqt5yiqOr6Mz04/AYat9WWO\nlHszf5d5UFQ1MpV7s0wwHme8mKe2KdorzekFFbvP0BatbYzZ2FYwkXcOTrJCk8tryyBoS0Tk9B0g\n41zbMyoePcN8KW8AUQyY3Jx3r2Yc+s6yRIZeYaw+MEphxNbTzLWGG3AVXqnaq+LslSD+JwNw/q4p\npuilo9QXiGvJ6AJNuvwG5HOLH/i69pegjZwbud+Rw6xLUVG/VXHwV0wFjs/SbhdCWXN9SujjqUic\nXRdjwfQ3l3Dm2qipMK97JchORCT6IQomNp1mfm04fpeKT9gpcrtgJ/fmZioEG7+MArwVrsy16QL6\nZnYWvLxtGrdg22HatGgrfeZ8kXVjxHR2pWC4nrcytnEP44eYC2d6wX8jZWy7CB42tXEOOM8nmvkl\nA7T3D+qY1xZ/5mxEAvjTpRKMaE/KV/HyFhbbEr+/U3FPiUXF3ktNNnsRCZvkmjqiWBdHR7m+vFru\nZ/9trHnhByjm+cYq5tHfFfDzC6ZtITGeuGgPOjO2v90CwjVMePFMG07SlMZfm656rpP/90lnprS0\ntLS0tLS05iH9ZUpLS0tLS0tLax66rpjP6QDunsU57Oh/qpKinZ90wt3TOEg6sDaGdOBt/hR6PLmS\n93/4EFhw/9OkQ8u+RJrwprdJb3a1kJKP9cahcbiQoo8+O0B2WbWWOffTEE66tz6Hv08+zr0Vx+PW\nCnDlu2v+Ws78mykHPw2kgUMstXTPxU1cd2YP+Oi8P6nliNspdOg9ABbcmYu7wVGKKyB13xs9oGJ/\nXxwdncUHVdwRhtNyYgx09GQeTsv4ClL1C0PAB7EnwA2/SmBM5M3QnjGmc7r87Py8xAMXXUgtOKM6\nB+QhIlLvYVWxTy8p5uYZkFeCM4gxqhZXjVMir48czFfxchtYqNZUPDFhO0Vd13rTr5PjfFZ0Hyns\nslawrks+97BGHOce2nkjjpv+ajBB5AquI2AMbJvdy7mLo4vAkEllzOWAKJDfa5PfUfHGNNDpxHlc\nmEedGC9J4aCdF6Np67gB5sdtU+DlzjVzCwPmTJD29+lbpGKbN27DhK3cc8IFxkylP/P3yGlcTPne\n3P9TJkdxznaQYsQ+EGG8JV/Fb/mAJBcU8v4BAmJylEZGwEJjrriUP76UOdVRyn25f535G3OZdnAK\nN20VKGWtrB1k/k6Z6hr6xFJQ19MZrD9tZZ0M7eD8tvOeoBbnpVYVB5wD64uIOJVzDwGhLPhdkzhE\n3Rp4TeFZtlyMW/jsM330d5oHOMs/G5x1PpT1JbENJ9jpz/Ls2tYFNq4LZM5+fHJusVFHKaiV++m/\n28ovXuccvZTlpjMLq1lrXEPBoolnwILWYPp/lcmROHkXWxmar4Dn3CJ5vg0WsQWj3VT8tM10Nm5E\nPltoPEt4TxGR7gzm7UwteN6aYFVxUy3P7KwyCiQ3r2FshHdx/t93FzC2H2xlPaospD+X5/4jPx8z\n4c9DuFa7P40DvclgLfsg0pkpLS0tLS0tLa15SH+Z0tLS0tLS0tKah64r5vNxxaHzd84WFX+7mPT5\n3nsKeH0wacncXlJ3JeH8fGEXafJv1IMJcu8GQ0VexlkykIYbyL8cxGKNNyGjbDDi9D/xt+1xT8y5\nn4FUkEO4qchYeRJukiVHSCemxROXduOC8LCQTo2oB1cERtI9ARX87TrX7SreU1+gYq8tFIwsOniE\na/PhfKLbxDFKGaAvX7aAHZc0cZ3tpgJ77ieJXdIpYHiDlcKsLjmkzEM9SLGeiiKVvMiFOGIUPFE2\nRF9YA0+quM6giOKKZHCZp8l1KCKywVScsrgV9+fmDFBV2Qy4yT0LvlH9NGPQaSU/X3CMz4i+jffp\nv8TPe9aCGAYO/f/svWeUHdd1pr2rc84BnTOARs6BAEgkEgRzDhJFJVKygmWNbEljjWZGM/ZYzkG2\nLCuZShQzKWaCAQSInFMjdgOdc865a350q56CPpukfJug59P7rKWlzduFuqdOqnP3e/Y+3GdpLjL1\nRC59+ZFmJKKv2vRx+An6722LP+7ZteEkuQweRC4r9p2lZqdIvnjQiLAqeYOEkStKOStzeBjp4bVb\nqdPVh4m8XHKOhLr50URYnXXpd6fykRVWBiP5mJlVjyN1jI1RZ+kLqOPmZto8637GY9YlJK1PO8wR\n25L57i35zCNjQ/T5sc1IMuGvcM3idcjQlo/cdDIciXC6CAumDJ9vpN+9mrXes1MWUobqI9RVTirS\nW2ga/TTU1x/b/5XEscu/RIRy19NIalmLSEAbPk5S28HDVZ79xheYEzJ3IKmFbLn8TLTy40SOJsUS\nVfdSL8kpD2dzzV/PYLwc76Ytz/QRkTZ8LRJ59gHm9fgerm+JIBIs+Sh14cYyvyfFMSaeC2abxT3m\nS64bIOd99x0s59lmpSHHh1Ywj1yMQW5tqfNFxTq8Q/uGqOOQECTSXIf34But9OuZRl3PXIos9lor\n0YyR40RnLj1PPypfw/xgZjZwgffUgg6kusEQ6j7uBiL7ttcgzWf52jwxhS0Pd/Zyz+CLRGonbGab\nQv3wj/m3R2nDkA0kC55fhVQbl0HE5/tBnikhhBBCiADQYkoIIYQQIgCuqMyX6pMGvpaO+/mRzbjx\nlr7A+m4iAfmvpQu3ZPZsEjRmhRCVVZuAa/CzjbilRyeQKiorkAjD0om8aSpBFivrxR260Zcw7XgU\nER1mZg9tx834ZD5yYGErCe6C/5ToxNrTJLULreV8q/pC3OybqyjfvvNVnl2xgnr5WTbu3Tvfxu37\nxjauz6pGDpvYR8TUdGlDbzjUXWQG0R3Vt+BKb/hnzl+cv8yXJLCeCLuM2cgfP3ue67+7iWixinBc\n2MN1yHnVeT/17KQU3O2hobh2l4XjRn7Bl2gxf5zzF83MXr3Y6Nmln6CfvnWEe9UE4Q6/uxNJIyUh\nzLPrapFPGlbwPC1ptPG+HiJjfv8i0vS+dCSyc324xlfOYByUPoIcZw/YtJGVjJTwcjbSVtxpxlRw\n4stcn0YE0LMHPuXZ912FDPPcMvrytTOQPRpqkP/+/G3+7SNR3/fs11yiaJf7InWK9yODl0TSF47t\nIJrJzKzyj0i4mFnJuO15lHnHFvI8oy9v8ey3xt/27ORY+kxBGZJJ8ZNECw58Cvm4suIjnp20jOt7\njD7SUO2LfiWwc9rYNPRpzz6YwDO+MUzU6YNHqJOtcZRzTxlSa3YOkbl5Icx11fOZr6P6kMSbIhiD\nxzI4my3+HSS/w2uZx250GeOXRomODG9CmjcziymiP4a0079SB9d79k2+yNHXW3i22ky2ERR0Udah\ncyT8La/4qGeHXYVENDBAxF90O3OQe5L6Goplnu2Pp79PJ7GVRLbO8yW5bEigj5cHkUg1PJmItBWJ\nJOPd7TvzcGMYcnfFMHL0eMWTnv1g8MOevc+XIPdQK3NiURVzXOgw7TScRB8pq0MKNDO7bynyafkb\nXDewnKi9yuOc81hQzniJyWCOLN+ARL72GP388QHeNTfVIUNnBNFWNVfxXni8hbXCHcs4d/C+U9TL\n+0GeKSGEEEKIANBiSgghhBAiAK6ozNedgwSSXkZE16IduFaLZxKptnMxySwnYqo8e341skfo87gW\n712Iq79uBf7zJ5of8uzbJzi/L6oKt+LdO4nuOFaEzBNxhp3+ecvXXvY8P7oG92P+a9gRibi7336a\nZwjrIVrtRpRESypDMjrdRaK8hDGkoSVxyI1Jh3DXH6kiAmLFXFyuA3G4T2fO9EtaW206iEwnYqSj\nAamxsgJpYM1GpKOzQcgEI3HrPTv6NeTbjauJ7nktmeiqnFYiffpWIZccG8Q9PxyHdHbXazs8e//t\nRH9sdYkQDIunPs3Mzi6+x7MbHqfNZqZSvub7SIR6+kmkHaevyrMHl/D86f08f/FppM3MDNzQlS8S\nX5m2kUSHq33nC3Z108eLl9HG00luFO2TUYP7/OS1ROHF/St9bSSSvplRjFyzrYwkrIXhPE/C8Nc8\nOzjuc579aDPywaos+pRDoJ1ta+O7clMYOAcOI9+vjkPCMTPb8CzjdtRFQir/CnWf8BjP/M71tHPG\nBSTJvHGiuEbG+e7mUKTH/b4yFVbw+/SxvfTVohTHs0sjGL8bxpk3pov28tc8+83b6C937KH9nmlg\nzn0onvpJmXWnZ0euQe4cfoQyJ97KfHUsiMjBis3IX8t2MNddWsA82xP2jGe7kSR2TPNFZvW1I7uY\nmY1Xc92+Jczx0U2ctXf1OWThEF4zdvg1+kXyN0jGXHyOZ9gWx7aJ8NOMx6CUHZ4du5R3ws+6ONOx\nYDZyUfyO3y7J4/slp4O+fCYOeTJllGjW0iiiX5Oz6b/lPUieW4LYWnK8kfaM76Tu693/6dlu3088\neyiBeW3LOaSzU/1sjwgqos2HR9kSsSEPWdDM7MU65rOr8qnvw52cZbn8FEuTuC1Xe/boCZ4z9UVk\nuLL7SeYaXvMDz65oYq5JGWA+Gp1JW/3xGNH0yecZFyeHmYSIU/z3kWdKCCGEECIAtJgSQgghhAiA\nKyrzHQzBtZa7h6ikpUUUYziWaLCZe0nEFrWCCIDcclzRx9ax+z76FLJKqYvr0pnA1Vk2hpR0XycR\naUdm4Yo8WoMLOCoNl2RuP2UwM3NGszw7LgZ379vRRJYsmOAZJpKILKk/m+/ZTbORldb4zvvbcRj5\nbCScqKpD3bjBr5tJAsTGIz+nbDm4oveU4PeergCw1lRkq7BjrMlnLORZqtqJvGrKJkLnpu0kWByZ\nifQQnswzZrQjx+zfggxRPI48fNWTT3t273XrPfvkN0m6GnsI6cFJppzB3bikzcwWPYa72lYgGRS1\n8Jydj2/z7PgBZMX+2SSF3GJEoZQP09eG5/I8F98k6ifhZuTY1kvIa12puJhzunE9x7r0WbObbLo4\n1UmizvrFRL3M/jHnarlhRDQNdVHWVXH0979bRVutreXzshr6QutWpKHGNvpv+feZE266h3407xAR\nZr2+qLI1BMra7vaqy55nsU9+eimaMbhsPxJFezKRTjl7aP+KJKSx0pnI7uXH6XutPkk6+C3GY0sL\n0ljKNdTR0lSe4WQlZxN2FFEe4gYDY68veOrjE4c9+1mq1D4VwdxyxiEad3QcuTP5Lf5BWsRcz97h\nO6dvTiLzdVEoWytGS7kmpoV5b1E3ff9iPRFlsQdoo4s3+ApqZteEoXmHnkLybQ9e6dllS0j+mtVE\nZGbRAtpg4zHa9ZUcEk/ev4j5YrAG6fj0r+hrcdFI0D0lSGTd/bRa86rLI4Sni5r5PENSGfUa5Xv/\nnC1C8ms544sufo1zWStv4h3VGUZf7irwbX/oJqp9bAZjOTcKebVmMX05/lfMfUvP+c47zGQcvBl5\neULdjMPML0PX0B82jSGFn0tlO0rzJaTNNTcjybW20Q7PnPilZz9snP/4ivnOxp3B1o54XwTjJd9W\ng+5w5NyOPvrt+0GeKSGEEEKIANBiSgghhBAiABzXdd/7KiGEEEII8W8iz5QQQgghRABoMSWEEEII\nEQBaTAkhhBBCBIAWU0IIIYQQAaDFlBBCCCFEAGgxJYQQQggRAFpMCSGEEEIEgBZTQgghhBABFSw3\nLwAAIABJREFUoMWUEEIIIUQAaDElhBBCCBEAWkwJIYQQQgSAFlNCCCGEEAGgxZQQQgghRABoMSWE\nEEIIEQBaTAkhhBBCBIAWU0IIIYQQAaDFlBBCCCFEAGgxJYQQQggRAFpMCSGEEEIEgBZTQgghhBAB\noMWUEEIIIUQAaDElhBBCCBEAWkwJIYQQQgSAFlNCCCGEEAGgxZQQQgghRABoMSWEEEIIEQBaTAkh\nhBBCBIAWU0IIIYQQAaDFlBBCCCFEAGgxJYQQQggRAFpMCSGEEEIEgBZTQgghhBABoMWUEEIIIUQA\naDElhBBCCBEAWkwJIYQQQgSAFlNCCCGEEAGgxZQQQgghRABoMSWEEEIIEQBaTAkhhBBCBIAWU0II\nIYQQAaDFlBBCCCFEAGgxJYQQQggRAFpMCSGEEEIEgBZTQgghhBABoMWUEEIIIUQAaDElhBBCCBEA\nWkwJIYQQQgSAFlNCCCGEEAGgxZQQQgghRABoMSWEEEIIEQBaTAkhhBBCBIAWU0IIIYQQAaDFlBBC\nCCFEAGgxJYQQQggRAFpMCSGEEEIEgBZTQgghhBABoMWUEEIIIUQAaDElhBBCCBEAWkwJIYQQQgSA\nFlNCCCGEEAGgxZQQQgghRABoMSWEEEIIEQBaTAkhhBBCBIAWU0IIIYQQAaDFlBBCCCFEAGgxJYQQ\nQggRAFpMCSGEEEIEgBZTQgghhBABoMWUEEIIIUQAaDElhBBCCBEAWkwJIYQQQgSAFlNCCCGEEAGg\nxZQQQgghRABoMSWEEEIIEQBaTAkhhBBCBIAWU0IIIYQQAaDFlBBCCCFEAGgxJYQQQggRAFpMCSGE\nEEIEgBZTQgghhBABoMWUEEIIIUQAaDElhBBCCBEAWkwJIYQQQgSAFlNCCCGEEAGgxZQQQgghRABo\nMSWEEEIIEQBaTAkhhBBCBIAWU0IIIYQQAaDFlBBCCCFEAGgxJYQQQggRAFpMCSGEEEIEgBZTQggh\nhBABoMWUEEIIIUQAaDElhBBCCBEAWkwJIYQQQgSAFlNCCCGEEAGgxZQQQgghRABoMSWEEEIIEQBa\nTAkhhBBCBIAWU0IIIYQQAaDFlBBCCCFEAGgxJYQQQggRAFpMCSGEEEIEgBZTQgghhBABoMWUEEII\nIUQAaDElhBBCCBEAWkwJIYQQQgSAFlNCCCGEEAGgxZQQQgghRABoMSWEEEIIEQBaTAkhhBBCBIAW\nU0IIIYQQAaDFlBBCCCFEAGgxJYQQQggRAFpMCSGEEEIEgBZTQgghhBABoMWUEEIIIUQAaDElhBBC\nCBEAWkwJIYQQQgSAFlNCCCGEEAGgxZQQQgghRABoMSWEEEIIEQBaTAkhhBBCBIAWU0IIIYQQAaDF\n1L+B4zg/cRznTz/scojfHsdxZjmOc9xxnF7Hcb70YZdHvD8cx6lyHGfzh10OcWVxHOdbjuP84l3+\nftpxnPVXsEjiQ8BxHNdxnOIPuxyBEPJhF0CIaeZrZva267qLPuyCCCECw3XduR92GcQkjuNUmdlD\nruu++WGX5T8j8kyJ/7+RZ2an/60/OI4TfIXLIq4gjuPox6EQHwIae1pMmZmZ4ziLHcc5OiUNPWFm\nEb6/Pew4ToXjOB2O47zgOE6m72/XOY5z3nGcbsdx/tlxnJ2O4zz0oTyEMMdxtpvZBjP7J8dx+hzH\n+aXjON9zHOcVx3H6zWyD4zjxjuP8zHGcVsdxqh3H+abjOEFT/z7YcZy/cRynzXGcSsdxvjjlfv6d\nnyiuEIscxzk5NZ6ecBwnwuw9x6DrOM4XHMcpN7NyZ5K/cxynxXGcHsdxTjmOM2/q2nDHcf7acZwa\nx3GaHcf5F8dxIj+kZ/2dw3GcrzuOUz81z553HGfT1J/CpsZk75Sst8z3bzz5d0oSfHqqb/ROzdkL\nP5SH+R3DcZyfm1mumb04Nbd+bWrsfdpxnBoz2+44znrHcep+49/52y/YcZxvOI5zcar9jjiOk/Nv\nfNdax3Fq/1+Td3/nF1OO44SZ2a/M7OdmlmRmT5nZnVN/22hm3zaze8wsw8yqzezxqb+lmNnTZvbH\nZpZsZufN7KorXHzhw3XdjWa2y8y+6LpujJmNmNlHzOz/mFmsme02s380s3gzKzSza8zsQTP75NQt\nHjazrWa2yMyWmNltV7L8wu4xs+vNrMDMFpjZJ95tDPq4zcxWmtkcM7vOzK42s5k22c73mFn71HV/\nPvX5IjMrNrMsM/sfH9zjiF/jOM4sM/uimS13XTfWzLaYWdXUn2+xyTZNMLMXzOyf3uVWt9rkHJ1k\nZr80s185jhP6ARVbTOG67sfMrMbMbp6aW5+c+tM1ZlZqk+35XnzFzO43sxvMLM7MPmVmA/4LHMe5\n3sweM7M7XdfdMS2Fv0L8zi+mzGyVmYWa2d+7rjvquu7TZnZo6m8fNbN/dV33qOu6wza5cFrtOE6+\nTXaI067rPuu67piZfcfMmq546cV78bzruntc150ws1Ezu8/M/th13V7XdavM7G/M7GNT195jZv/g\num6d67qdNvnyFVeO77iu2+C6boeZvWiTi553G4O/5tuu63a4rjtok20ca2azzcxxXfes67qNjuM4\nZvYZM/svU9f2mtmf2WR/EB8842YWbmZzHMcJdV23ynXdi1N/2+267iuu647b5I/ad/M2HXFd92nX\ndUfN7G9tUkVY9YGWXLwb33Jdt39q7L0XD5nZN13XPe9OcsJ13Xbf3+82s++b2VbXdQ9+IKX9ANFi\nyizTzOpd13V9n1X7/vZr21zX7bPJX7lZU3+r9f3NNbPLXJziPwW1PjvFJhfO1b7Pqm2yPc1+o01/\nwxYfPP4fIwNmFmPvPgZ/jX8cbrdJz8Z3zazFcZwfOI4TZ2apZhZlZkccx+lyHKfLzF6b+lx8wLiu\nW2FmXzazb9lkuzzuk2t/s90j3kVa97f1hE3OuZn/zrXig+e3mSNzzOziu/z9y2b2pOu6ZYEV6cNB\niymzRjPLmvrl+mtyp/6/wSY3NJuZmeM40TYp6dVP/bts398c/3+L/zT4F8ltNum5yPN9lmuT7Wn2\nG21qk4NffLi82xj8Nf42Ntd1v+O67lKblP1mmtlXbbLtB81sruu6CVP/i5+SLMQVwHXdX7quu9Ym\n29M1s7/4D9zGG5NTex2zbbKPiA8e9z0+67fJHyxm5gX8+H+s1JpZ0bvc/24zu81xnD8IpJAfFlpM\nme0zszEz+5LjOKGO49xhZium/vaYmX3ScZxFjuOE26QscGBKHnrZzOY7jnPb1K+oL5jZjCtffPF+\nmZIRnjSz/+M4TqzjOHk2qeP/Os/Nk2b2B47jZDmOk2BmX/+Qiirg3cbg/wfHcZY7jrNyah9Nv5kN\nmdnElBfjh2b2d47jpE1dm+U4zvvZ6yECxJnM/7Zxqg2HbHJhO/EfuNVSx3HumJpzv2xmw2a2fxqL\nKv59mm1yr+m/xwWb9CreODX+vmmT0u6v+ZGZ/YnjOCVTgSILHMdJ9v29wcw22eQc/LnpLvwHze/8\nYsp13REzu8PMPmFmHWZ2r5k9O/W3N83sv5vZMzbptSiyqT0Wruu22eRK+i9tUnaYY2aHbXJwi/+8\n/L5NvmQv2eSG9F+a2b9O/e2HZva6mZ00s2Nm9opNLrTHr3wxhdm7j8F/hzibbMdOm5QH283sr6b+\n9nUzqzCz/Y7j9JjZm2Y264MpufgNwm1yD2KbTcp6aTa5/+235XmbnKM7bXKv4x1T+6fEB8+3zeyb\nUxL5Xb/5R9d1u83s8za5aKq3yXnWv/Xlb23yB+vrZtZjZj82s8jfuEeNTS6o/qvz/1hkvHP5ViHx\nH2XK5VxnZh91XfftD7s8InAcx9lqZv/ium7ee14shPhAcRznW2ZW7LruAx92WYT4TX7nPVOB4DjO\nFsdxEqZc198wM8fkcv5/FsdxIh3HucFxnBDHcbLM7H+a2XMfdrmEEEL850aLqcBYbZPRCW1mdrOZ\n3fY+Q0TFf04cM/tfNikhHDOzs6Y8REIIId4DyXxCCCGEEAEgz5QQQgghRABoMSWEEEIIEQBX9ADX\nj9z5N56mmDXvV97nKT6pcTCz2LOTysjF9o2STs/+i55bPbs85wz32U7ezfbiNM/OXcC/jXqxxLMv\npJ/z7JlVfZ4dunCxZzfvTfTs7qhD5iezNMyz87rI/bd/C6k1Eo7GeXZGYotnn8uJ9uzjtUspx+7n\n+e4R8kdWJZ727EXz53j27F3dnt0a3swzBPOcqSOve/bnHz3qT076H+ZL//BdGq3ohGfGls/27K6j\nJLvNPdfv2QlLej3bvYZyDh/lZIHUOXTN3grqLa+BNCdns0hkvnfGJs++Y88pyhO/xrO3NT/p2Xkp\nl6cEK8ts8+zM01d79mhuhWfPtBrPjg/p8ey32zgabNEakjk/e/5O7pP0rGd/N2urZ/9tHf1uaxIn\nKFw6m+/ZzQk8Z2p2gmd/7eFXp6Utzcy++9I/e+058/lg7/NPhaR49sp00gKtO3+Jsq4jAXV6K2Oq\nLv4rnj0U9F3PdsoYR0MrqQvbxdGW8Z8hsfLEXqKrnUHaNi+ScdrRcfl5xePxZChpK2FcjHfS7ulj\nbG9sTGecd5Zf8OyPZMZ69s/PlPNv517j2cNn6c+LckmbU5Xh5S+07t30/5WLuGdrCm377c2fmZb2\n/O4fvuq1ZUpjvPf597Io/yeXH/HsoZ61nt3Tylhe0MecdnAMO+wzzF3tj2R49tGu7Z69bDH1EzXC\nnNaXw5wW204b9TzPOJ19x+VHL6YdyvXssvPkgMxcUMB3BxH305nDXJB+4RnPXtm1zrOPL+dIuNYG\n2qx2xknPvjGDft3/Ov1jtJhronPJmpLawTN/9AvXTdvY/Myf/sBrz7FQMnlEOsxHkeN8/oXwRs/+\np17eUYPd1HdoM/Nr1ecZaymHqIvtQ/TrL7/AfV4u4NFCP9bh2d9oZjw9mkGbJb9y2fF7VltIcHRI\nIvNlhJvk2Qde4521eGu6Z8dmM6b2PMG8MKtrg2dnz+KdGHaW8dW5lrHfMw978ARjsyiK92xUJOuM\nbzy48D3bU54pIYQQQogAuKKeqdRkPBU1F/k1H5zF6r6jlfWdO6vUs/+mldXzkTZWngmxyz07bGGl\nZwft4pdOUTor5uNFrKTdXrwiy9KrPPuvq/EsFW3iF3hmCL88zMys4h3PbCzhGeL+udWzz/jKlNvE\navjcd/EwzLmeX1VB87s8u3CYX9u/X7zEs1/dya/Hp9bjydtyiPNBm+LwhJxdjJfu8zY99KTgWcw6\nzC/BuSlHPXviaur9YD7l7Kod8uzsU4959r78BZ5dcoxfC4np8zy7cyXeq0udeIcKIvmVcmYxx7al\nFb3g2VEH+bXkjlzumUor4b8vttA2c89vpHwP8h2zY4579rq3ec5De/k1uyQVT8DIwArPPlLOL77w\nOLxRr4X/0LNXtfOLquokfdD9BJ6A6eTVV/CQtc2f6dmfiiMzxIlL/FJ7+iZ+ITY18svzzhEclmGd\nL3t2VBW/eOPvvM2zkw7jBXsqBq/WjL3U0exY7vnSGX4gRkXjlZ6RRF8wM9t9AS9X2CCelBI779kn\ng5g7Mp+l3/au5tzcqjE8OLe3LvPsx8/gFYlvxVuWMEEfmejHE1C/irE87PNSRa6Y/uDf/kL6ZtUS\n5qX8s3gszpyZzz9owPtY+Wk8yyequM9gE2Pwnn9+07PH4miDRal4jcYP0fYdWcxLxYcZW039zGMf\neZB54IUTl5/i1BxKnY6uwUN2rh6PdV8Jc/amCMbOHoc+e6qTduoYo/0iOyjrg230tefHbvDsE6nc\n86sTjI+00THPvlTnVy6us+liKIqxFh3NGEmJRtFoG+bd+vdJvB9378Fjc10EnsNHC+ibDx+lnaNG\neEOkx5DO6+DGT3v2zdvx3rQmMH6fqeKdnvEKqtJIMmPZzCw7hvZsfhuPX1zaW5798TuYI4428c6+\nuomyzutZ7dlnH+CeRw7jjV0UwbuvLQ2PcOclvM/rRnl/xdUxHtvjX/OV+t3O3p5EnikhhBBCiADQ\nYkoIIYQQIgCuqMx38yyOUHoqEhmuu5YD4NN753p2TTxSUl0L7tqUObgoEzNx48We2+fZA71ID2+0\n/qtn54bichwKwb3Xlv0R7tn1tGfn7MANWfUAbmkzs9vPU47jNZs9e9FVuPHz53FI9rGdbBj8zBok\noB3GJufUzgjKNExdfP4gbtyrHSSz9CE2BV+4jo2RM5Kp36HtuH2ni0+04W4+9ibu3e7vIKW99XfI\nKFs/hnx5wkFWiK/G3b7ORUrIi8dtX1ZBv3k7mnpOj9jr2cW1uGRHaz/r2XsHb/fs4LRdnt0zePkR\nirknqyhTPlLSaC9u5dkvspG58EEk6J25tPfNEbie92ZTPicVd/jIjwggmLc537Pdw8jG+27HTR63\nF4k79qL/9889Nl0kxiFP7j/LaUhVcXzfvVU7PHs8P9+zJ8qxG9bSzkmXkM46fJuZy+Lo41/pxa0e\nOXO9Z/dewG3fY8jCV8Uw3tNikA6Prr/8+MSgx9l4nJZL+cYq+Y6bUhnPSXOQAF6OQkpOPkz5TkQc\n8+yMbOTCjnD6Z1MN5V4ai8RS8AL/9vwAMmJRDxLudDXnoiHkr4oWgm+CRqn3at8G5JUtPG/bnyOX\nlt7KsxyrQwprDEOC6WyhrwwUIP/dlna/Z5+uZfPyrjuQwvKeZSwfegoZra6AucXMrDCbOXQkyDc2\nfdtD5h5Fnnsn+Kxnx3YwJ8Zupo9PDPCcHbf4pMNXsEuP8/yrZiH3NtcjOz0zE9ny7iieczq5foI+\nf/T8Ds8OW4b817KXd8hIIjLkyrqXPLswmOd/cMXPPPsH4bwHt859hHueRtq7N5Lxkbl8xLOfOch7\ncziIebBr5TbPXnqJLSpmZnldvIMvzqf/7ztFfyg+yZaHdREEcR0a8wWQZCHn7f4Bc/NXC5g7X8xA\n4k/oo/+PT9CPMgoYy9vHkePTL/127SnPlBBCCCFEAGgxJYQQQggRAFdU5nu6E0kqivQSdvUDuFZH\nX8M9ODpM/pOhRUSiNJ1mZ33WHiILmoKJBku/+UeenbIbt2dzDm7ZwpQDnl1ZTwTe1nQkoLEFyIUX\nnsPVaWZ2cDYRMWFxSHidO3Gb75rA5Zx2nIipkbnIfPn38Dxzfe7u00GHPfuq4XzPjkikTB9pQoas\nzsSl2VLGM2Qdxk06Xbzz9hc9e3QLLvaot8s8OymJZzz0JLlFEj7JGr7qFC783CD6R8tM2juoFrf/\nkgO49huSiaSJcxZ59lvhtGVpNi7luBPIRQ2xl8tCof3Ik68s4LobfC72iF9wzc5h3M1R2biGy5bx\nzGHVyJmXjvHMHcsYdqnzkPD6Qnx1wTCw4hxk2uZmxsR0Mi+EcgTHkcvLrafPpmbiSg/rpu7P9SEr\nrTrJmOrrvsWzx0sZE8vfJKfX/5kg71DpP9Hf0zIZy9uLkLjvzGc8VRYTKbvwHeaBycISidXkko/q\nE6V8x85y8tccTH/Fs50LRPn+NBt59u4B5qnYS0gmZ+p8eeVC6Ydtxyjf+XSi5FYWIPktnXd55Np0\n8Fo8ZR7fdb1nh68nmiunkTK/toT+vrSXOS5mgL5fPJM5ZOdj9MdZpXye1UXffLJjj2ePxFDPhRP0\nreJi5kw31SffnWV7g5lZbTXjxS1Ezgp+i75TUbLFsx9I/6+e/dQBcsYNJiPZx40zb0a+zrx50KVd\nV62jT1VtQ5oPzUP+29hI5HCjg0w/nSQd2OnZQyhhFuwiUxcUITVnD2C3L2KspQ8hz6XtZEw0jjHW\nHr5tt2f/2YAvGjuG+S6ig/fp+BlkVNtM/bpvo1lfCEJGMzNLHGFur/QpgJsamKuPpCKR5/fyfnwr\nhvl1SwrRmdfOQZLbG8FN54xRvvPPM2ctWMj1v0xh/hpv4t1acO7y/FjvhTxTQgghhBABoMWUEEII\nIUQAXFGZLyKRBIjLz+CKPvLM//Ls1FO49Go6SC+fu5HIhfVLiRIa/D6u9//xB0hb/3iCBJtH5iKf\n/F4QO/oPjhLd1Ry53rNzBnA/Z8Tj0l4Q6dMmzazSIRHj1uA3+L4Y3ImzW5Aolq8l0qumO9+zz30Z\n+eTMCo4FuDqB70uqRTJYfBG5accKonVmHOaanlW4Zefdgnw2Xay8A2ng5WHcu8EXcQGHRCGDJucg\nveX/OVF7E58gYem5GtzQZQlEaq3qRQqp3EIUTtRLuKebEnCFL7qF8hxuoTzBacgCRbH0GzOz7hT6\n4/U/pC2PrcUdvvFPkDTStiPHJg3inm4tRyJqPE6E4W13EjHzchVu7tqXkBWyc4lsGU550bMP5HEs\nTa9zudQ8XQzVEVE7EU/fKUpB3njcQYqZX0FEacUd9Pekv6SOozPpv32F2CdRP+3aE0gJbVfzh/CF\n9Iv008gwZaH05U1lRKxuv0Q5zcyKYvidmNVB+2xLJMlkfSwJUNOG1nt26TKkkYRHkMPqP47E0P4E\nkUTxmyjTUDNynjuCtBkTjTR0IZr66mzlmUkzGhh9/bRB5Tj96KY6otAmdjNvnLiBseDkEhF9JAHZ\nruhJIqRC11Z5dlU1Ebu9ebxOomYhp146wrPn7OZ5G31H6USf9x0jNovPzczCfZHZ4S51Z+kkeQw9\nh7TVXEOyyfxQ+mzVy0iwaat4hxQ3Ix0mjTAHtVwi6rL6Zu4/WsZ8nRiCXBQ7xPtqOvlREnNHUBvt\nOVTHs0WPYg/NYx6ZsWe9Zz+7iXaI3kzf7O8g2v3s275IwKO0Q9NdzAmZ833Hr5QxfpMHSFR64b4f\nUJ5y+oiZ2TsXSTa6zHcEWoMvsDXhRub53hrkvJJ4IkZ3D13r2eM76D8xEbwLYlYzFkoi6f9r43xb\nUB5lDs5L593Rv+C3G5HyTAkhhBBCBIAWU0IIIYQQAXBFZb7BeqSbs8mPenZd1ac8O+U23Lhf2Evk\n3asn2JWfWY879fvLcbEPXkTmigxGhvroOGvG7Qu5z8ZTuC6rJpA5WidIaLbdISnZ3BUkajQzW3WI\nyIeT1UhOMXFEQFUG4UKv7eHfV4b7Ti3/su/8vovIglVtXNO/inOvTv0Qt2SU75TzllZcnfcfQtp4\n1efSnC6OXMBNnDAXSbU7+SnPDskhgi/8HG715nu2YrtIn7ntJGm9sZpou2ZfdFlYNdLO2/chHd4T\nh8xz4DBtOTMGyTYrmuSijXlEKpmZBb3OGXnVdyPzrCzlutAjRCqGJCO3lRRzTUcrEuzqa5Fsy4Zx\nN8/ynZF18SR9ed5i2njvqZs8e2SMaKjV/ZdLzdNFQwpSWusAcubxDL4vuo+x1heL7PPRQ0hGFffj\nSj+R5jv/8CWic+bO4vlbfWdnjU7Q98OfJ0Iy6CbqOuR1IgT/94O0x3xfAj8zs6R4IkxnvUl/aE4l\nuq3vHDJGTD51b+OM05hN9MOsUKTgC4VEDOWNcX7hoWgil1a1UO62GUg1tcdJdNl5gKgy+4xNC3ML\ned64Vl80m8t4TKpiXPzBCaS9xnm0wYl2IiIfSOfz+HT68kgKkrjbw9x9/DTXb2xH1nEWUv/Ve5EC\ns69FXotNQo4yM0vci8z/1DxeWTfFs2XjZBT/PieLPli+Azvu4zy/+ziaUtI1zCnbnqNMi9YgL8X2\nUYb8Eso3sA9Z3x1ifplOrhkmkWb4AHXcFUG5H7sbuWzDd3hvrJmFXD54knntTAXXbE7m2U6+zrib\nuY4tN41v8s6JvZFx55xk3DzdVeXZyaHM8bFnL9+akJVOFOKlcq4LvokoV+ck31GVReLlaw/wrvnp\nR5E27wxC/izrQP5rzeN9tGrgXs/+q30kVL51IXLuy2lsFeo7wnrl/SDPlBBCCCFEAGgxJYQQQggR\nAFdU5svqxF3Ztglpb17ijz27Zhfu2uZw5LKwNJKjdYXiTv1kJRLWU3OJHiseww0/8guSck1E47pM\nj0NiqKjCPT+09gueXXCMs6T2pxD9Y2Z2VRTS1VgVrsyOa5A3lkbgTj/xxjrPrm9EhlzoSwD4ZgxJ\nD/97GpEvL9Ui4UXeg/SwshK35L7rkUV/sJNr3DjOv5suwgeo63VtVZ79fOiDnp1/lEioN/txhy8O\nwc2btRd3c/PVPOOZfKI3o1suePZgG67qL/iScPak0Q9mbEaGGH6B+m9YS3n6W3yyjpklhdMGVcO4\nd8MfQ8IauxOZIPY88sz+bxPxWfZntF9TPdJT5HGuGVhFX0n9JnLI3tf5bRO6HNltxjv5lGcT50ZO\nJ8uWM0Z2dCArls7heQZ34g4PGUZieb4N2SN7lHInvUU9Rm7gbLellcg+u2Jx52dtYHy1ZiFTH3e4\n/4JVRHA9eInxdL7+cik7q4qx/eZiIq4KIvm+S0uRdmNf5RyyfS4y55rZRCsNliMZBXW+7tkXChl3\n1x1HCj24in4Y00w//HQ2c9NTM5GSzO606cD9Y+bH4s/Rlq3bOa+w5U+p63feofzxwcs8u2Av0ajf\nXkw0250nkd1Pha/07JMZOzx7xR13eXbz+ec9u+gc2x7CE6nPulqiyNJPE41nZrYjG6mmYBy59Mwo\nbVObxDjtamMcvb4i37M/tY33SVMBEcV91fS1+WuIYOx7C1n3xnUkIT1Y7ZOKh5717KxitnpMJyUJ\njKnjIZ/w7JawT3r25n/4ume3FdBuHYPIdpdSqJcZdYydx0d5/gdu4QzCC3XM8bnJPOdEE/Pufp87\nZjjpm569rJ6Iv4pCogXNzOrf4F0+cwP9/3QT7+AFqb5x9zxbCtw8pMCoGN6P1W3MzUMhyPfdTcii\nLbOI1JuVwBx0cYAtAV2xjNMV61iLvB/kmRJCCCGECAAtpoQQQgghAuCKynz9+ezEv/YsET1vzMIV\n3TMXt1z/G0gjN4/me/abc5CM1sThDvzoG7gDG0rZ6R/xWRKAzW0ncm7vOFLPmWxc450HcdV/Ogr3\nYXwekoeZWfZOoiD6CnEhD3bhcj44l0RmK+ORUuquxeWY3k4y042tyJ9VIVzTvo7EclUqbalGAAAg\nAElEQVTHiGJLyKHuMv4G2TFlDc/Q3oc8M12EJRFF+I1aXMa/H/xnnn22+37P3hiG+zQnlySqqb6z\nsOqCkSwrM6nrzBFcxnmDyDn7VtHeA9uRVg/jkbeQDuSoT5QhbcQE/fSy57lQg+v5hrT1nl3Qjqv3\niV20R0gyMk//f7/Rs1ccYUi1jyApDxX4IjPPUNbBJ0gkGLmJPjgUSh+aNZt+47YhMUwns14m2WTV\nRqLBnr7EWPtKG1FowyeInnRLcPsnxSHt9SaSFDe/k6idunb6clwF0Yxt1STUTGsl0d+3cpBbeoNI\npBeegFz8fNflkbZpmSRGXXqOOSU2nbmgLwVJpzsHyWnJHCIYyxs4szDsIOUw35aCnNeQCU7M4pk3\n9BAldyia9t8dgRSe6BD9OV28fgf2aCqyzdWp9LtDvuSywTf6oon3InHW5iAv3RfL8+6NR3YPzmee\nLe5nrIS/WOXZ/SG3enbWPM5WtBDkpRlt9PHeetrVzGzpCqTEpjOM/5Rx5MZb9lGO3rlIR7NyeZ52\nlzP/MsZos2qXSLjeeuzWSvrNQDz/drSUeae1lzNKr+6mrqeTFxfQRyL7kLnGgj7n2eUreeYbQ5nL\nDoUh4baeYk6dn8D8UhiHtOv0EUWceIkx1PsjJOjyryK7J25Awm1Zhbzm/hA5vqYASc3MLKeFSLqq\nJqK5h4aITgyZyfy8bCsS80+a6Uu376AuOuYxrnN78BHFl9FHnoxG7p+VhMzbfo4+dWMP/aK8NZ9C\nM2T/XeSZEkIIIYQIAC2mhBBCCCEC4IrKfBmhRNK8Xok7rTkNN256LRrNjSVER0xk4jLvfoEomZ67\nkOFODvM4Mc8QJbXgD0kw+dI47uSrXiEZWOJmrr+/CZf/kUQib3JfJ5LMzOzFmUhs608QiVYXh4sz\ncZDolfp5RNWV9iPpOONEDIYvxC177FXkuaSiHZ596+M883fvJ1rjoWW4g9+qRDJpu/vyc+img/Ig\nJNLbRp/w7HMnkba6r0X+GWkhsi2uijLvGUIyyD+D63m4GgkmPI72OzqB9Nt+lO+akY5k8KVhpJnO\n2bh2W44R5XIy5XLXc/DncF2HXCLyrOIa2sMdR8KqK0E+CH8KeaNhguccXE0/Kh7o8uwzacgEH7kB\n1/jxWGSVA4dJtniokLOmIhu55n14nt83P4gnwrCVr7C7TuE+/+Ug9b1xGfLpuC+Zbeo26n5OCDL4\n2TpkrrGLuOcjih7y7HkpjK+WbsZy47gvOWMd0sOMBNr228t8hTazd/4B+b/rs/ybI2cZ/zPDkH3X\nVdCeZ0f4t2mRjOv+xcw7a1qIjGtfzfPHldLmL15ECs0Z4f4lZynPgYy/9ZX6UzYdJJYjlx/opF5K\nWij/3Q79bk8L4y62xCeFnKH8p1KQ3lY/xzaDyjGkwxMOc9rGhXxvUALR2m8c2ezZKdlE7I4uI9I2\nbOTys9yqT7N9YXQm3zHzZcbRcylIQctXMXes6eL6Ny4yR3y2kHbaVsx9ZiVs8ezOGcwJUW0k87w6\njG0Kb47/vWdX9JHkdDq51MiWjQ0djNPjGS9RvvO8p6ovMQc1xjCOVt7MGbjnT/JsdQPIXINZRFVu\nmYmv5WI5fTZxAXNZTjlj81Aoc3nyUrYNpEf6I1bNZsznPTWjkYja8Ht53w8dzvfsF33ydHYm2wKK\narlm92meeVkn7dlN/k4r+Tlbi+beRVknihgXdWHIuSva2dbxfpBnSgghhBAiALSYEkIIIYQIgCsq\n8wWHV3n2rH4ksthRXLGlb+Hu3YfH1Sa2I5E9MITUs/f7JKdcMscXUZfPOrH+V0RMhcQRPTayEXd4\ncQ+uyBMOElboOuS/d/ZxHzOzuI43PTslDbdxy/VIA427cWXviuPsudARIi7mccSfWS/RB+lVuBmd\nE0iBQ5uInruzhfrqy0GeWn7hbs/edgSZzAguDIi0PCSSc2lEg6ztIsFewmxkvvZG2nj/YiSGeT/H\nffzyVuS/j/nO/hsZwG2b7NJlw2dQ/81j1HNbKdFJrXW05a47cRHfEY6UY2bWbPwtOdiXYLMGOfpG\nh/4SlMu5e6c2EkkUn45M3fwoLvNZW5AuLjyOLPicL4p080pkxOEkJMmlvSQI7Wm7/EzB6eL6Ptze\nh3196mA1/bFuli/5bSbu+bqDSKGDl5BAiuOI2ov0TTXNdyBT5z1K4r24JdTj2eF3PLu9g20A36im\njxxJ5z5v1NBHzMwW3UwkVnUbcv41viimvl4iMp/YSnTb/FKSB2YdIgL5eDPnnB0MQgIreZnIoIuH\n6fMFJYyRhgRkqLYs+nzQYiJep4uUCKS6h4oow+kuohR3+/rd3HhkGzfnnGc3ViGvxO2iv/fPp96z\nZ1CfQ/u5z3ZD/im8lgjMzIXUz+kK7vNgEHPj9joitszMGo/RB2dvY+4Inpfv2bcv5d/v2knkaGcO\n83qcL2p8oI8xOHqIJM1jEd+nrFmM/Y5u+ubLSWybWHaOSOzTM6mj6aS0jrE5vJB5654e2vm5Yt59\nUdHIfyOhnC2bcsmX/DOe92lYOBGWYamMm79ewBz8V99Cpn71v2GXjd3s2Qt7kPJfeIl5d8N9yK5m\nZm8M8l4rnmBsd1zgXZveRR+4ZhSZ80QFEZN7FjHXxPoSjzZnMZfPqETa676RsRxVTnvuyuI505qY\njx7JIGL94/beyDMlhBBCCBEAWkwJIYQQQgTAFZX56opwiVbuwi234RiRZ/vCcJWWBOMO725d79lP\nRyEfFN2Me3ffOaIpFi3wuTSDkC2W5bF+HD2A235nONLZZocEkzVv4FYNi/Ml7TOz/AbKGlVI5FJl\nEzJTYqVPWroFiebVZ3C/1yXjrgw5+5eeXXQ/rs5Yl+izKp+7dme/L4phNm7MgSFf9IVPnpouxrdz\n//5sXP0N83zn1x3GPR3yGVzS1zQRGXR8DmVeY0SknJ6HrDleg2x6oZdIoq0hSC2DBUSnZOQg2YRG\nIRetDvkjz248i5RjZpbXSKLK/V+hndrCadfYCiTlt/twH98+gZxX9zhyb0kW0l7ZxR949twNn/Xs\n1Ahcydt3UXchKfSJS1HIM6fjiJK5z6aPozE855ET9N8bPsF5mqP7+O7Ul2/x7MRgJKym5cgEx9Yj\nWybt5nmu2UVbjSyjPRuTuOabQbj//2IICePnW5EkKpu4/7Ik+peZ2XA/klNuKHVcd5o67illzOe9\ngVR5YSfRRjenca5YTt8Gzz6zlHkhP4Qo5dKZyM3nL9Buud3MKUmZ9Oe2k+spNIpMQNSsZI5Lr0Ty\nGZ9HUsSU7fTx/nuwa35MW6YsueTZY3nIK52d93p201miPeM/tt6z44aZoy5cRFK57YJP/ktmHq/u\nR+7ckIAEaWbWv5x3xZ4MZN5NUTzbL4ZIZrupC6lyVxj/9mqXeSd0AWf8pdUgzRfcwXza8iMSWJ66\ngTMxF/lem3Uzadf6cfq+2VabLmrHkMzmBjPXHg1lzrqxlyi06nW0c+4hJLlXTnG+XmYqUmVmOEmR\nm8JIOvu1MiTSzm+QXLN4J9f3pDEntKXQF4pXnvbs3f2XJ6ZdwlCzF3O4V/R5Ij0jSpBzO3J5T1s6\nc/uM89TFBZctH5/M5D4/K2ZtccNuIiH3DNJWV40yfz9RjNQ4fz/z0ftBnikhhBBCiADQYkoIIYQQ\nIgCuqMz3zM9wb38tBwmorpOokebrX/DsGaW4FrtP4ma+Pg0/YXrRP3v2tgoioC42IgFcNeyL+vIl\nQOtLIEnYnGFc2vFhSAahq7kmvI8oLzOzs2m4CpPHkRWWPYYr27YgsT1RjlyRloeLe/ZF7vvcItzP\n3YNEn2Q37/DsrDSkwxUxPOeG3ciZR9fjJo0twXU9XaTGEw2z+nS+Zz+7EFd6WAXhmBfexN0a3kS6\nyeQ1nIXVXEWEWP9z1NvyFbhzR7pxHzuFuPyj6mjXsm24lbuvRloOKSP5ZdpSIuTMzJqMhJQpZT5X\nr7vNs1uikBJWT6z37GfrcY3PuJ37nKvlLKzYi754kHNIhEHX0Zeja3i2WSmchdYwiJyctY6zsMwu\nj3oKhIko6njxEJJy8VHqYkk88sszPbTnRzYxfifO4epva0Uu7G9lTDnNtE9vLHWx5BkijP5+OUl6\nx3zjLPeV9Z4dHoG81lxyeXsOpjJ2ekOQ5/NKGKclbUx/LUuYj9r6kbAbIhg7qSlII9Ej+Z593nfP\nJb6Egc2hzHeRDlJXeyh9tbmPBL9mvgyDATAywXNFVpD4t2g1bVDxMIkwYw+z5eD8bKKd43Jop7lB\ntGtoIm0/a4R79nYg2ZzpYkwsmPMZzz42zPfmVyG7RF6kzl/ynfFnZpbxOaTHlbVETg4eftSzb+pF\nXn1hHtHFa0Z5hmNBvE9az5A4NuE+5spnX2P+Xf3xX3r2nJcXe7ab96JnJ9cSlZ1RwFw2nRwOZRxd\nE45smVXNIYxDNdRrdgzvn7pkJNzRdmTnmPXcvzKLa/IOkMAzde43PfvE9ziXs3+mL4K+gzl7sA5p\nc8Bhrs2dfblse24F768tUUQIHz3Dv2nrRNocP8HYDgni+/ZmM9d+Khm58bDDmIo/SPLj6lt5x888\nzHaBl3wR/qVPM8abUi5P0v1eyDMlhBBCCBEAWkwJIYQQQgTAFZX5vr4AN97Z5Ug0Y10kzdpwhiLV\ntuJajCog8qZjFRLZa9uJaMg/h8yV6ztj6J1SonCCTuNCTuzCpZk7C/fetqZ8z3YqKM8NhoRlZlbT\njCvyYB/P0H8HbsPrT+Aez/fJmRM9uJatFRf37UUk29wXw7MtCCFCobYXafNABpLZwXpc3Z+toS6e\nraM8Nk3HR8X5JMj6mUSw3Rq8wrNzl5ON9M1Mynaym+eKbsc9G9eCpNYTgjt/33wk3sJSJAm7RH8a\ndWi/7BVIn3OLaO/nwylD5sTlku3ys8/6rqMNwkqRefujiADsP8Q5anPn0PY9jdR7XjhJ7JpczvZK\n8SX8LO8l8ih+FbJobSEy59FwJKhZRz9KoclFGTBvhSDX3LcBuTyijXOrLlzkeXJnI3v8Swvy5OcL\naIddl2irbOMZ6mOpx+B5xCReuIPIo8V9jLs9J3HVz51LYtqjE1yfE37578LmU0QnZqzib9XVjLXR\nq5GP4l7jfL2wKCL+BhqQq5pXIT1uPM1YfmwZMkZYEJLiQ/2Pefb3emis2HXIRFHnkYmmi+IwIiST\n66n3yHZkxKwRElJWViEpRz7M85a+RbRsaC2RqV2fRnbtb3vDs2MraO/1Ues9+4W9jK2ZGfSb1dG0\n33eWIH2OnvElGTazlvPIQveNMl6qz/0Pzz7gO0fOmnxbCs4geSUWINlm5SEdVW9DLl4fw7hrOU5E\n4pph+mzZft5FzSuRvAZcZM7p5OtHKPdzsfTNWxYTbXd0nHlqdgvRq93ZbHG5/mpk9Lq2Vzy74yLv\nx/ExogJ7xogsbw9BUrwqkb4fNkjdvZpCBPbEed+5lN1IamZmLUeQ9lZ2kSRztk+GbnoHObP788jE\nV/V92bOPH0RGPxpOn5nRibSbkcXYHDzHmuBcL+uPBxO55kIe4yK4g/fF+0GeKSGEEEKIANBiSggh\nhBAiAK6ozNcQgbQS8ipSTPRaXMthaZzjk/VSrWe33IMkd/ExXOPzC5Gb8rNxDYYex7VYsgR3fux5\nyjASjZTU8SPctfmbkY+SiomGOfIb56I1hbHbf/VgvmcP1+MGr+gliqd2gmi+uxZ+0bMHVv3Qs6t3\nEK3VUYS0N1FGksC+jxCVsjIal2lkJM9wOp9ImeWncdFPF1XLcYc3PM5zDaf5zm8bwyUddpFrguOR\ngkou4D5vGqGuNi8n2earBcgH40e4z8klRO1EO7Txi/30rQI8z7YwAlkhOOTyiKEL8T43djHtd+Is\ncsCJLiTJkv4fefbMCuTepiaiUcciPsH3LabcyT1IvyM3098PbKOP3zVE+1XsQT4YCkWmmk7WD/Id\nF/uRhYPCcJk3B5Fs82wO0t4dLv3xR2eJPBq+xPUrtiJzla8hImfVT3xRbm8gT8XOIcpxpJ+zvA6n\nMGVl9SBbHD7hj3I0++LvU6YzP6Quh4qREtJ8EltVKf1t3QAy7M9GSSobM0h0V7vd6NlL+1717P5+\nZKXXx7lmSS7Rks31yDbhRfTJ6SJ4L/1/+23Uwxzf+ZMVncwn5bdTp9cdZ8yeDCUh5VYXaX7PNqTs\n6/8n0smFR5h/WvNpy4d85WkI4vOydCIft04wHvMykNnNzE6+RZTc04sYI7fcjC8g8wTbJvoTGbMZ\nqTxP2+1EpvYcoA0WDdHHi0Jpp8eamOP+LJY54d48+viRt5CLslZeHrU2XeyMRWp3xkkwmniEd5OT\nSLkHkolg3uKyRWI4yCfTRzDfLWsm+u2V2citg89zn1Vr2IKxL5W6q+9FFkt0fWcTXk3/cnYi+ZmZ\nxd2OJNe6G8m0uhrZMmIt/STlOba1OCPIkMvHeJeP+pIwHzMSe65awucz32S+eCmBPnnsbeqlsxYp\nv3bZ5RHC74U8U0IIIYQQAaDFlBBCCCFEAFxRmS/KHfLsyHRcgnFHWdNVZuOSP/cArss5zUgDS3Jw\npecnI588XYQE0jiCq2/WWdzE3b4zjJpSSLaXczNu3MxxynCw0he50PCLy56nK/OvPfuvkpC3CpNw\np1/TghwUVkmSsV/5zmorLvWdT1SElDh7iGjGYx/HRZnXg2zX9irRLSdLkB2XjxO50xCLK366CHmb\nNuuJo2ypC3Gr9r6IKzVkBnXqhNLtehNwK++ewGXc3I+U0LEHl29oL7Ju4TgRWA2Jez07KYWIr6JC\nIpVOGbJTejSuczOznnVIPg1PErUZsgoJ7/pfELky0UViy6M5nB2VuYb7HEzHnb1kJzLJawmf9uzf\n20Xbj44iE7S9iNRWuA4p7EwDMqLZAzZduL6kh4u7iWLKrXzcs9tLOfPr2gqi5eJW/MSzC2YgH9Qm\nMHbq63HJNyVXefaBPM6/yumlLjoHkFRnNtBHuvqQ6b6Zt8Oz129CjjMz+95uooRC1tDuzgBzSuNs\n5MOJw3x3VQR9NSmY786fRbTlaBht1XnYl+S01CeFZiDnzup7yrPdBJIh1jdfLk9OB3NHkVWODHH/\n4hrG45l85seel9lmkbmIvrYohjnx3I3IrrkhRFGV/Styb1gYfXz24XzPPr2KvjX2LH0rPZd6rkrh\n84kE7mlm5iQjB44sZjx/rZIy3RJEO91SyDzywjnGrFPF9oKMM8i69bM4FHFWJltLRi4xv1zbxPUV\nLp+76+i/rTW8T6aT5BTq9c5mpLe6bOaX7hDqpeBt5oj9wZwvGLeBJMJtQWyJcZp5nsIhpNqTNyJn\nxhzimtIa5serRgo9eziZd3TwXKSzV3dePjbz32SOjB8j2nCGLwF1yCDlqDvIXNNyDXW8qon3wpEx\nZMXutHzP3neY6NojBUjEM8ORcC8tRbZcHYssHnSCaPT3gzxTQgghhBABoMWUEEIIIUQAXFGZbzwB\neWo0k4Rg2/Yhadxeme/ZwWFXebazmORzERO4Cb/3I84bmhuEvLa50Jf8M+x+z/6LJa979p09uC4L\nHBKB7krg+lm+BJMn1hGBZ2Z2VyNuxj0nce9HhHKv1jHKFL0ed2VxNjLGziaSXi4IxsWdEoVUMf8s\nssLrsTxbxt3URegZJMztZSSJzCki0s2s0KaDruWUbd7iKs8ufwv5dtWXcLdfvIQMWltD5E3JCc5L\n2voxEs9dqsKFH5VJPX85Cunh22W4bbNDaMurk5AMLgTj/g43zuNKmqB+zMy6tpPNNGIhbTlxibqe\nfSdu790H+PzWFiLVdp6lvwTPQ/LcP8KZX3MGiVz6Rg/yZO4A0uOsDbjqe0uRke6qIfpvOhlZ6Us2\nGkIE0O4wnnPWUn57/dUexuM1nQ97dnbTM57dOEx/XNNNJFXQG3/o2aFZ/8uzj1zF2V61u5AMHohH\nJhqPRo77oyik7BMNyOlmZmeKibKyXb/nmWMpSFpXZdHHhoMYFwdC6KtJ8/iO8Xjmjvwe7CNLVnp2\nVT7SQOYe7tmY8VfcpxGZJMf1JdSdJt6MoU9dVc080xVCMseg87TlV1Nv8Ww3jq0F285R/vm7+bf9\nJbTl+VEiyhYmI5Elx/kiVpvZupDu204xZLTxwgvXefahPF9CYzMLzlrv2Y09VZ59TQfXZYRx37OV\nzKGz82ib8P305c4VfH6mmYjKBAd5qi2Vd0tVKhJhzzjlXn+OekwuQDqaTgajmVP+JZHyfb2bufZi\nLNs9BiOR9saMvhB/mDHhJhPtnn6KuSkxhznVPYGEeaCQ+T5tlCj44YqnPftQJHV651P0kTlBbA8w\nM6sJ/5Vnr51BH3tmL9Gj8xYjQ98bTZLbtji2Nhx6kWfoL2I7RkIhcmG27x2xZj1t+EgkkeCZh5GR\ne8qIllxd/Nttj5FnSgghhBAiALSYEkIIIYQIgCsq8824xM79+hAimsJ80T2vDBNlsHIfEUCN1biB\n3XTcrwt8sk/SjbgGJzqJHKwu/Zln/7ezt3n2i1HILRfzcYEG7ccN75YSOVh73eUJEw+8xL/pSUei\nKgwnAio7h3OPhl1c5UPv4FpOjiFCIWg90QSDPpfzz08Rqbd21ZOe3fUYiT1nJh/17JZEJNLIxdMf\nZRKVyPOeeRUZbuYxkp4Nvoib+PBs5JlblhJt1LGMtjxSTqRWeTPS58da+fwfVuJKvyucKIzzO5EL\nv1eKdPLVXPrB/vNEfh5bSL2ZmfUW+KKYamnXVxtI+lc5lySM41lIGqMjyIfdt1C+LUeRHtM6kQhr\n4qive19d4tkpvsjGnNn0m8e2028GFk7T4Yq/wd3ltMPjwUQ3rb8F2erYs4yX/12IhLf3Au72tBYi\nVmctQe5+ZRmRfSX1Ozx7304SJib20E/nr6U99hyknZPHfUki5yG1Bf8MKdTMLK6NdgsO/7lnZ29E\nMozax/PErUaWSvsFcsXitURxvXQGqT04yxfFdRaJdGwlbZ5YQmTjS7Xf9ew/aPIlz7yFyLjpYkMl\nZTi7HOm4fj1j6hNlSETP7EcWy0r1nWXWTD+1XObBGZ2M/bHFXD/UQX0+ml/l2eNxRJ2d+w7S//pl\nyEsdNxJ1FVZ5+dhMPo/8U5vI+JpfT787c575NCuWZ6u8j3mnP4J5Ifko88XaGM4X3H+Osi4Oud6z\n80Z5Lw1cQJrqWEJEaHM1/WA6CQtDqv29NqKZOz7C9o2PHadtT4XSDgnBnGu5rNV3pm0p4yC6mc/P\nhtIOJ67hPg8f47vqJogKLFvqi3Z+m37Uv57tJ70ZyHRmZnP3MbedbeOsvQVXIf8nh7BWOLKVz3tP\n8B5cdgff0biM6+veRv7N+xPav/2P6Xsja9im0GK8Z6s/xVachc2/nWwrz5QQQgghRABoMSWEEEII\nEQBaTAkhhBBCBMAV3TP18tyve3Z7A4cJ3xBChvLOUbT5rAxClHfOz/fsBcGEppaNsc9kvJbQx6Vj\nhCI3P/VfPLshhNDHhEj2VSxdw96F+BlkDT59Bs15Sy3ZZ83MmvPR8pNWo1m3uis8e+wwez/ibiVT\n+EAw2vfaixw0edbQrwvKaZ6DaYTr73mCTMwz5vjC6Zt8GeOjznj2S2WEgNvlyWj/w8QcJ8Q8owAt\nOrycrMadbAeyGwvZDxP+DHuS4tegV38+Fw38yYNkLm5axf6vkHr2OjQ0s7ct4/ep2y070PHLOjk8\n84DvUOyHh9hfZ2YW3MI+twr3Hz0766Ydnh37Kn0zbYK9Ybu6CP1OOsg+g5d8Bzrnp7Jfo6KdvRWf\nux5Nv+w19gjumMEzzDtP39xRx/d+ym626aK2kXQSm0rpO7aL31tNsez1OeByTeoy9lNENLOXqKGS\nMbEhlj5y0nwZp9ez12VgP3sU0vdzeHBX3K2e/ULMTzw7+TH2Bt00mznEzOxAo2+fje9Q7iTfQb6d\ni9jHFvoT+kPyZvZ4PDnBXqprYhh3QwO0T+QWro85zjVzG9lbsjKZ0PK3e9l/M2fe9B9c/Z2bed71\nNexjad/GXqJf9hOevngVA7WuhvEbfwtZpRN20fcnwtgntGeUNl6YzBhMSeC7lo9zn6QF7HlrG2Lf\nSkc79dBxgdB2M7OSTK4rrGUe6U9jn9ztSxk7j7/JnFtwlhQowQ3sveu5i7l15AJ9Ivln9Pf8B9kX\n2TjB57XJPE9c8GLP3hh/+eHp00VQInP/aYc0Cf076VPvMC3Y4hsox4/reFdOzGfvUeFpTvOov453\naGcfqWQe3E/ag5dv4POHB2jnXT9lHDir2cN2Noxxc/HU5cuMxPWkIlj99mcoXzUPcbCFPZnHCukb\nMzeScuEd357U+x59yLPnBT/q2c0vsjfs6L3cM/cx7rN+BntyW/Yyl7/czx7k94M8U0IIIYQQAaDF\nlBBCCCFEAFxRma+9Dsml9B2kgX1Xk9U5Yz4h1NV1uCKvX0rW1cpnCVFfWkpYa1Y6brnjDfd4dlIx\nksQsX7jrnuNIAyPtvnVlEFl969N92cPDkQXMzGY5hKwPn1zn2R0RuKkHSr7n2TH/hLwVtuJuz466\nH0kruS7fs08OIhOtiEPOKwtFripJoEzu65Rn7yZc921V05+Zd147KSmeKCSc+NZZSADOJdI8DL1J\nxtmKj1R59oneuzy74GVku6hopL2mguc8OyGRflCXR5vFnEI+GDYkq4h8JM67u2nLM4248M3M9iXi\n3n0oGjexbaNt+tNwdR+qJ6x7eDMHK687T9/cN+cxyvHSnZ69fBXjoOrivZ49OB95wj2Cm/xwKc9z\nd8/l8uR0UZOQ79mbUkmH8MxeZLg5M1/z7LiV93l2z25c5ucafBLWnTxz18GfeHaUkWW7JhUJ87ab\nkAyefolw8JwDhDpn3UYqhcGPUkd72nH5m5nNr0Deqo9ClugbQA46cYK55oHN2Odd2nZmNnJ2yxwk\n2YyDfPfWw7RPWf4/ePYTuciTV40wfltjqKOKRxg79hc2LdzTjGxxJApJKv88Kb9uQq8AACAASURB\nVDxub/2SZ7/qy9oeMR8prLCKeovoQbJsCGfezK8ka/01i3Z4dvd+tkRsv0R27oIIZPqQcNIqhDb6\n0hb4QtXNzIab2GrRv5Ex0nKK8f96GxJT0bXMjw2XkJQy17Bt5MDzyJZzspEOUzdSX+UdzEcvt3/M\ns/8oi6za57Je8uwLWVwznZS0MLdd6uJUhZURizx732bmlI4TbCP4QjBpLI6V/4tnFy/n1X8xASls\n3WnaOTaXPhvfSkqCC4d4L232pTy5sGCbZ/dEcYrIhmjaxsws6i3m1J8G+7YzzKDcjQPIzUtmk64h\naidpdarX8X3fucAzZARTX7knafOZqTxDdMZ6zz64kfLYKHPZ2hPc8/0gz5QQQgghRABoMSWEEEII\nEQBXVOb7Ly24EL9bhDt5aQmfj7lIKZbsi+r4eySG1kIkrNQK3IyHxnHdxl/CNXw2BHfg4gFcjqmD\nZNp1Du/w7EuZuB5X9SMrvHqJqBIzs8FNSFeDJUgXi/6eZ4vPIdN710yiJoaHcD+feIyDchdehwu9\nYAB36oUkIh3yThAN1ZREGSLuJvt06I+RsRL+K88/XcT42m9tE3V0doSorcxOovPSH0TOWHeMeszI\nQI4ZSUZemVXNPct6abPYnb5s8dEcYNtWQ/RX4Q3ICqEDyK/DrWQurjfkGzOzTbOou9MNZL0emoEM\nmV7NMxw8ud2zZzQQwfXdHOp92SEiVeLzcFtHn0J2HQkhUmekBrnI1nP9rcHYzRWXS83TRVzms559\nrBGZKM2X4XrDBQ7E/ZNG5IarRphGInKJ4ip+iX73ziwiXBNyeJ6J87jeX+1APon3HSxb7ktunu4w\n3oMuELU0eowoJzOzp79KVOyWfcwjQbX0yTnXEelVvRiJobOK8iW1Iau6XdyzKAQ5oKGZbQpjK4mw\nGh0n+nHH28xrix865NkdB+g700VbBlLI+sPImts28tt55CJjcMFZpNaBif2eHdRF6O/ZSA6pD7uO\nsRbZyLx05CSS+oqjHHhdks5c3JSb79lOE1F0r2ZxUsGcMeYEM7Pee2m/Rc1PeXZnOGVt3sc8e341\nmduzGzjxYnQG/za6hLE2mEo5Ituor5wmtjJ8zndKxf5zVZ5dnoPMWVPINpM/NGSuQMnqZk4tD2Kb\nyvhVHGLc9zyHk8f2I09VpNImCUvYdhFegXQW1Mec1ezbUmCjzE0z9yBNn84l+rwliXabcQjZcTaB\n5XY+ERnZzMxJ5uDjjFD6UnAL/S0tnHEUOspcE36BLTuXhnlHLBvjfRo5Tt/OLKLftgxQLy3hSJ6R\nj1LuiU9/xbPr+ukv7wd5poQQQgghAkCLKSGEEEKIALiiMt8/FhCJdU8QERunm5C5Nvbgrt8ehQSQ\nk4MrLnsMl+OxhURoZW7HPd9wC5+vbiAqIfYS7trrUnzu9khc+FHViZ79ky52+meFEGFkZnbnjh2e\nXdfJoab7b8HHua4d92tKMzLGUCnyUWg8EXBH2nFF9sfw+blduLHnLqRMseW402suEmHVjiJji579\nKP+BVzogdnUj/xwcQS5JSsKVvHQzrlfnMBLnS4m09y0XcaU/2UbUXsZs5JiKrnzPLnFxvTrOLz37\nugL6xMV+3MVVPbjwk4JwW7cuxNVsZpZ1kv64pA1ZYfTcuGfvy0MKHEwhkdzI1UQ9rWzArRw38h3P\nPlGPVnV3EvVyfAfu7Dlf5HuLtnNw55uJuOpjO5Gyp5P+WUgx0eVIiUcHcKWfWUCk05q8az27sR1p\nejycKLfhmJs8u7eVdp6oRYZJiaLem1by2+52X7TvT48yb5RegwRblYR0uNQheaSZWWs4cmN1AZFi\nCRNEaK2IoA8cO00y3+sKkfYeXcJ4LB3h+Y+/xWHII4XIahPHGLNzY5Aq+4L+2rO7d/F56yYifKeL\noV4imCon6Eezxplz/rKXNl41RITd2KkMz47+IlJ2/zNIv2Oj1InjS/I4UcP3vp22nvsUIMeWnvqh\nZ/fOI+Hl+pc5hDxq2eVjM/t7yHknw2ibhjja9fZFvMqeG6jy7NzZzE3DvoOb5xezVST3eQ7MPpfH\ne+biQ2wL6Kog+aUdZovD/RNEAe/7FXVkDI+AOXcNktzic8z9ra2Uo7SVqOCwjcjLXQ7Ja6Md3hWd\nKx/27NmPfcuz01bwrtjWQjuUJBI1fzGbcXpzA21bWVjl2W+2Efn65Q7mfjOzHUspX0c/c1tLM1G7\ny3OZX89U+5LKbmae/8pzbMd5dKsvgr6Y9/Txo0iYzkXe90vyif492Uu7Fdc86NlH1nEg8/tBnikh\nhBBCiADQYkoIIYQQIgCuqMwXGYkE0F2Eu759LxEtj+DRtE+E4iutiUEuC+9DGimOIqlg+QDyWsnT\nuJOTFuC6rlxCIj07hPt4vAvpqWsR7u0bGpBVmgcvj+ZrGCcKpiUTqWNRF0nDztfj0u+NR7pYeBr3\nZtss5MkZ23BFuxtwY8fFk5QtspvPs06j5+2Pw6U7c5iojNOH/BFgD9h00DefCMy1e+hGTS1Ew0T1\n4QKumcOzbzxFdMehDOpn3m2cNXesD9d73EFfxEgq0YunN+HCbW+g49T+3/bONCqu87zj7wDDvi8j\nQCwDYgeJTYB2Iwltlhd5kRfFS+Njt7XbOs7SNk3apD1tTtI2yUnSNGkSJ3Zry7ZsyUeyLRFZu5AE\nEloQCLEj9nXYBhiYYRj67f4uPe2Jz5mpPj2/T8+Rh5n7rvf6+d//805x7qPxLA5Bw4FELc6/i9yj\nlFLNPnyvKY+0781enEXbe2mzMnIGndF0QovnzyJJ2fJwEm4aJ2V8YRE5xGcL8zrsHWTOm4GcI/X8\nLlLss5NIuZ4kf4I+W/JBfklZwG1zrQO5PLYS+dOpq1XYr1hfCTNIsvNeSEMFgUgVE0sUyJ10Iqt8\nuB7Zfeta2t+SQDHImkW+M75v+VYW2M38j7YxnjOd/E1NGNKgKZl1/qNpxvbxJgpd1g0jqygzcsNm\nI2eYNfQgYcQazVz3GuRM701I5KsP6uRJFCa3iKrnVYnmTcz/sED6dO0SjteVBRSa9Q5G7ox4nzba\nlnDFjd7WzXd/9nHrq8jdr7xHsciGWJzY80HIycETFOAcW82e7pxdXiyxNpR9N3YLzrYWL+Sf6/NI\nWBurkHgb+7jnLAQxd0Le5xUSx48Z79h3cWk2/py+i1CM08oI9mvrZ+wJkYPcZzxJyihz2VGPHDrv\nIj5XhNy4VyfhWixIbGu7ue7fbf+1Fj9ZxgJ2zTNHVhv5/tlO7lfJvszxy97MZdMQjtigMdbTV4OR\n45VSap2VPbWzCbnNLx23dFAUrl3zVdzcxkb21wuFuPwesHKvGT3IPdRgRvIzlrHv+lp4lWdHOn9r\nH2ItbBhgHn0RJDMlCIIgCILgBvIwJQiCIAiC4Ab3VeZLaSFVOFyP4yo7lrfyvVJJLZ+s4cyzZ4yk\nfn/6EJLMhkZSxQ8a39bi88WkLi2f4wxI2kbK0J6FHGe9RdrbNYwkZbPxW/YgUoNKKXUhB2fg9lOk\nU61GUuVtQbh1Dvji+hnqfkeLq1I5w2xPCqnls3dJudp9j2nxPhPy4kQxadZnrpLGfDPl61qcGYp8\n5Cla40nD5mwmjrhC8dI+H9rbEU9qOHcYh1hCNOn22vcZm60JpP37U3F5zJhxD81+F2n22iv/osXn\n/CnG+sZOigpeO0WxzMS1yx0me27hsLvUS3rXOwZ59dICfV0djNsw4wzp5ieScEada8fBd2GMQnc7\nOkk9W/YxhyqzKfQ3GI5UE3GbdbOQxXz3JDUu1uOoHRk2vgmXUNAuHHkGF+Ps1J0nmRhPoU6jTpII\nzmZdT3cgl10eQv7ckEHRw24zkmyvLxLZYCCS2rd9kJUs+UgYSikVmYnkYK3lrLaIF5lveUlIw6OV\nSIGpScj/PpHIVeneur3jNDKG8x77RfEu9ouoCb5nPgWZKLAVOdPkpTubz0NM+rKv5Xrz/8vTBvox\n+Bptt6ZyvlzUL5E8rjzO+n0qAFncNclato4wN+fO86rDjSzaPt2KCzI2h3XXEc4rGoG6M/T84hh7\npZSa8kHaU7eRcNLacWfFvY40HdGB8y4ujPEIuIVc1p5FO39Yx2/vTmNuphRw1unKFopFTulcvVXh\nfE/BT3UysAcJ6WTu3C5k75+ZYz6m2HVnFtrp45xhCns25bAfbW5H8jy6wFx4egN73PV/1rl0y5H1\ng3yQfPt17lizlflltSGvBpqXFxpeusfrAsVLrKO+GfbCt23sBa/683vDa/lM+hDS5ls+vPKRF8l9\n2prYpcXjH7IuMhOZR3HJfI8xlH3Eq0jn4FQvqT+EZKYEQRAEQRDcQB6mBEEQBEEQ3OC+ynyuANJm\nE4M7tTgijMJqly5T9G7eB3fXezGk35LfJDU+Hkqq+Fwg7o7H7lGgsTecz9/rIo0ZYzVrcVQIqUFn\nMq5A5+fv85mty90aeYcoavZpPunKvaGkOLfaqZI5bMFxMp2EvPFMIOnqI/64Ff7aC3vPXV3x0LlI\n3cFH1aR6bz6IK+WBKT7fucfz0tD2j5FO25JJQ8dvoXjafD9p79y3kL/uJVJ4rzWYQqYxOgnvooWU\ncUEWEszYJdoboSvUZrj8DS1+qQCH3JDO+RlUyP87JEx8sqw9dU04R1ek0F8LMzgkx3YxHnsPIj2G\nrUXmvFzKPAq8gnzwcBauxTpf/n2Ni3Rzi410+I57/PudEfrF3///xzGUHoFcEdKIpD5VhMvPdxJp\n8/gIUs+mMObCXDOS2r2VfM+zA6zf7z6L7LN2kvXeduVDLe6roUjtti/j/vGvQ6a7VPBXfH5J57RU\nSv3JEOPQrHPeTcyXa3G15ZAW+z3EfvSla+wRJ5b4W+MI83x2O3Mv6hru2thJJJN2M+3cdp3Pd2ay\nP4QFISN7ikgvrjPyKOO3ai/XdnEHczZ5Guko2oe9uEx3FmnVLI7r+Fzctc61FGdscTAPvmlnr393\nAjk2WNcPCxW8lhA3z/fcHmMeKKVURiFnkQ4O8LqDXyqFUxt+wLVuL2YPDTiHtDcegGyVFMIYlzaw\nvqam39LiVQ+Ua/FJRfHfLc3IjukG9qaacN0hkh7kvO6s2KA8nOb99czx/B4+s5Svey3Chls26gZr\nsMeMDBs0wP1kpIU1vv8BJNIqXxyZizH87V+8Tf92fI11sPpd5mD6AgWtlVLqTjDzp22Wdes390da\nXLKbVyfq67mXB/rSzkoj+3lWOC7Me+3cl13tnMG6q5T7Ub8f1xARiUTY18R42rh1fCEkMyUIgiAI\nguAG8jAlCIIgCILgBvdV5pvWOdtmIkmVutpIj5bnbtLigFAKwiW28Mb973NJ9b0QRBrb1c5b/3UG\n0nuFIUhw1xy4fJ5cQiK8tJ7nyvpjOBRCjaSYnxvUVRRVSp0wIKWZY3A79FeRsk5L+ZEWHwkjhfhU\nCNLF+WiKmg2QiVZ3nUe1uNNE+jXShvQQWkz6Pa6TlOn1U7jY/BL0roQfKk+wmMk4JTfSXlcfLrQY\nb+S26t2kyYuvk5J1GXE2xWRy/cPBuIT6I3SODy9cZxvHmRM3NuNY9Lbgtim6hTuyKg0nmG3m5WXt\nKd1EsdH2JNLexiPlWuzsQGKayUa2i6jGCWpfYCx9+7q0eLqatHXKgQ1a7KWb1+vmkH+cRuaTXzqS\ncNgF3flfHmT6FHNk/lH63nyauXYoDSfNq4+wdcwmkFb3+QZyYd2BIS3+fjeyUlQta3D2GhM+tFzn\nNutBJjCOMr9m8su1eOWpN7XYz5c+VUqpf+jCiZWSy76T7+jib85ToK+sCLniwhLyU6oNOaB5nDU4\ndxb5N7SMtv1MV8B1exeuVT/deZrzjaydySjPb8HnophTJiNFMhMCKEA6Gco1PGVCapypYI9bULhi\n7bHIf5YR5EuvZqSjCt15iD85ryuW+Qbyysp51tbiddbp4EocXuEWXMxKKeU/+7YWTxjYszcqirlW\nlpi1+Kiu0HLmfsY+ZRhpr+82MnVWEHNlbgr36sBB5sSVMn7XlE48081rB4EuCtwq5TnJ7zvzuGKv\njSFh5i8if9WX4yj1qcR5OJ7AHO/6OnuHY4C1ufLca1psjaHvz55Hkk15kviOF3P5cjmvPhy/i8z3\n7XL66PAl3MFKKRXr4N5vLmG/9B9hf13QzZ/SJdrWiCFbVfSwZ5lWmbW4a5ZngqB4XtloKuvijy8g\nkfo4cfmV5vBqQo8PDuwvgmSmBEEQBEEQ3EAepgRBEARBENzgvsp8tjlShStNpOimEzmHbGaRtL95\nkHTtj80UCdwyxPlkny8iEyVPf6zFYcGkdA0xyArpbchQX3kN98k+nRK2eYHUZV8GLrRRG9KhUkp1\nrkZyKgtBAjgXjysr7SnSycl/T3qzOQ231u7zpGvteVx3fCTnFr3TQPsfcnJ9rkdwd/X8llT8j9OQ\nXr5z+e+UpznlwoG41U6KXmXRp2cG6JMJM1LVvehfaPG6pvVa3NBu1uLmbV1aXHEVWaGnGOk35Sbz\n6cp15tCUC9fkSSuOlE2dOFvGZ5YXebyok+2yLKSbbatwG+4MJWVcnUGaeG6UoqvZV5DCouKRPapj\nSXvPNtKeZJ1ENlyLC8dYxDpYe5e5MlpIvyu1R3mKrEKkmI9qzVq8OgM5+sEuPt/hv1+LM/0psOe7\nlf4q/4jvic5FFpuz4R5r9qKvi+ZYy0vhuLMuX6QgY/48e8IUCpayYzBSSim1vZBim2Mm1vDYacYk\n8VmkhOMnWIPTCofh4AyvDmxczyaxYEXCm3qO8TQfp9BlXhN7TXUeDtaAcKTDo+nsCZ46dfGxNl0h\n3HSuYaaX+bIzB8nvpxP0785Y5J/TTqTW4UH2ujdcyKDtTUhKNaXYn5LzdY66y6wblwWdJjeXve7g\n8Ada/HKmbj9RSrXYmJuBo0iDw524tMNCkbDSV7IGm9t51SB3AtkyJQu5sek6c+V2NntW2Tjy/bei\nKGxalcJ5qEXHkAJnE5kHnuRQCDJy+HvcyyIOIOfafZAzB5KRcJ0BFO3M+hUS/HR0iRZ7N3IO6ukx\nCvMW5zOPbnSwX0Z50ebWBfr6eYPu7L8Ofjc5F6e0UkoFpSONtlaxNrMDuI5oB3PjsjcyXFYC49kW\n9ZAWH67BAZr3Mq8prDqzW4unjvNaQMUE82XqSfpu1IrMOaR7JeiLIJkpQRAEQRAEN5CHKUEQBEEQ\nBDe4rzLf/BgpZJfuMW5wPWlWHxtF2ax2ZJx1A1/T4hZDlxY/XID7pFsnK13pIkX9yFpS+wk9pLTL\n7/I9cZ3EIy9QxMtwEhfWvZWcBaeUUjGZpCIXRpEA8hJJ449/hByUXEx7rtZSYNSwF8dU+Rmknt9t\nIi27t4R2+sYjTzSdJ00amfRnWvxYHMXd0jJwz3mKtWc2a3FfBWnbuDCcV3mFtN1yjjTsaDep+qAk\n+s0rkf5xdJJiTYjmeya6cbk1GpDC+szIP/86h4PveCTX4wjXFVfsWC7ZzhuQEsYDadt1Oy4/uxMp\naHwB91hGKvJqbQauxWALbfOJReoI78Lp05KGPpWxFznPcI8+6o3BXeqIRT7xJJ1rSN1vu8XZkjWJ\nSMrrP0V2nvd7T4trg3AbTTlwl+bHUJx0tAFZ4d908twr2UgpR3SFNrdYmLP2DRRVnDuCLJaQSPyL\nguX/X/iVQeZkQ1+K7r+wvoZb2P4KqTWqQjNf5beHOB+0OxBpyJaHo3jxP3TniA0h+X5ixp06GYJj\n7FsZOMb8m5D/PEWnruhstAEpLH89cc1h5lryIK6lk1nsSw8EIxHO1SG73l2DVGf6JjKP3xHW5r0V\nOokwl33f/hay+67ruAgPFDDPLiukQ6WUst1iTZWGUjxyKfNFPrQfWcn6EWMz4Id4ekg3lmGRuMhm\nklm/qQ76ri8OF5nFCzl+qhEZ8s5qZLG3HawDSgi7z2gnfXxG5zZ7cZj1UuSgDZcHkFv9I2h/XAf3\nBO8spPauR5HgC1rou6Q29s6hJV6DWMhHyi+oQcIb3IiL7tlAvucHncvvP4+ZWJtGffHM4de1uOdx\nPpN9DdfmzF0KgObnsKasYUiBed/jtxeSue8k5Zu1uL2ddbdzgNdO0nspFvxDX+btF0EyU4IgCIIg\nCG4gD1OCIAiCIAhucF9lvvBwXAYX5smr76wireszjaPnaiLp5PIyUubht3UFHbtwEPgP49YqziLl\naDxNM0/54gZw9ZDSHdQV/Zv8nPRhnglpz+DA3aCUUoP9yC8BDs7F6xnTOfIGkDCrXb/R4h2bcGhc\nHeH6mqOQuryDSe9eDtWdydaJ3PRiQ5YW15We1OL9nc9q8a8CzytPc9WoO9sqAAdf0wiuvTknbYwv\ndGlxej39HjiNfOmVw1lbgSeQe1sDSTdHhZPyv5RD+jg2DmfLiTrGpUNxDWur+d3sUj6vlFK+KLvK\ncY3U8/5UpNb+Sdps+wBpthLlWBU7+SK/csZ17CJzNiQGt9jipFmLu526MxcDdK6aWtbKiRxdVVcP\nsqoRB+rhZhxpKS6uo60C2S5mlGsKnkc+TYin77xSkTk7WpF0ftOOe6YyA5lg9WkkT9caXJ7ZochT\ngSau8zftyPpH23V9p5Q64o20l7yqS4t3WHHxnDQgb8TNIGM0fMbeYYpD9gp8j+KD7aXM821BzO2P\nMmiz/zpdAeL/Qtq9NoMc5Nr3P2yIHmB2FGk6wYgUdOo88zelASnIuBrdNXsJGTQglfn7QQLzV4Uy\nDx46TbtqSuifzZdYa0kxyC5Ri8g3If6slY4g3LjhDp3mqpRKrUCC74pgj4/5GXNt0wXW+dmVXGty\nGmM8kk/uIKUHN+NoOXPC2crn7/TiwI1xcA0Fk+x3rfHsQQdiPC/ZKqXUin3Iin97k3vCId0esaBz\n8/VHUPx2u4mxGgrv0uKbCbwukHoTF2KWTp7t7+D+G5xM+11G5oVP0UdafDFY50yeoe++Ucw9Siml\nZpxItS4T67RnNU7t2+d0DvxoPjOrGKurugLJC37sC4YK+uJmIS5UZwdt83mC+TmcqSsEbWVsk8/r\n5vwXQDJTgiAIgiAIbiAPU4IgCIIgCG5wX2W+4jlcLzV2pJuwWNJ1Sxtxp5XeQj5IH0BGW0zBYbcw\nTbp9jd/3tbhuFHmufQOfD/Ei/bjLH4dRgz9nu7UfIWVs9MbZVFy4vNBjXi9ShKNLl3LWXd/FILq4\nPgInS3Egcl4/ZkMVcoPf87+iK5RWRLzboCt2F0LaM+YcEmlLBAXKnlukfz1F6T7S9Y7TSJCG3cix\nKbdw2AWfwD1iL8PBNaI7N/FSA/JKcRrtneinny2bceEZh3G8vXD5d1rcPomXJj6CYnu93YzRyC4k\nGKWUGvGmOGXcDC4e05ptXMdHuJ6yynA3jT3M93TM4kqKbCI1HptHv9wzkYYuHCIlv86HuXwkDNmq\nWiHbpDWzDjyJIwzZs7AIqcp+iaKz/lOsndUrkWdnLpm1+HgW7VlZh3wU7YU8Ub2WPgqoo9DueAj/\nbzc2Tprf1KmTxcJwwj1gRZL5MJOCf0ople6i2OZkNONwYoWuGGoN19rkj3MtYD1zdaqVtWxxIpk9\n64U8dyqN69urW8wnTlHc8ktpyIWDD/Gdcd/Tyc0YidxiJIz5W7ibOX/1NpKP2YTMEbyE7GwbZW8Z\nOI/8k6grsuzrpSuIOk+Rwy0o4up2Bv2Q0c7nP1jDet8djSy4OENR4vB55H6llOpS7C/hdZwL1/cg\nbahrT9Ti3DwkeFXFZx5fh9P0tIE9yP9j9uj0Jca44Y/5zpLD7Ms2K21rSzJr8eZLSMXqJeUxltKY\nLx3+9I3vldNaPGtCtisJwm3XeIJXU7weo9Du7rO8WnPnCebI3XrWR8xG3LtjfThnW+fZj/JnuIem\nJzCXo3QFeBumludsAsK4rwd445yeruQVnMQSJLlDS8hwhZm8ghNfxX48sZ72V+rcmZENSHUJuiKx\na+tpQ08bE/fnYXw+Znj5/f4PIZkpQRAEQRAEN5CHKUEQBEEQBDe4rzKfdQFXwp48Uu9zcbh1pk4j\nf80WcQ7Z292k3B424faw20jvnzVwZpIKJNVnrMIlkNT9HS2+E08KdC4ex8DGEtK1HYrznE6dPLys\nPdm+SBGGgEotbu1Detu5yHUPx5CubJunENmaC6RZHbHlWpxSiFPk7gB9dyMLSSJ/qUuLCw2kZa/a\nSN13bCWt6inSbpACH9VJO/Za/j0jGWnn00zS8/vmcQCtGCYd3DpDMVLbRfpt8bkfaHHif35Vi4M2\nMK4HrWYtznPw72lTFBL0eQGJ7Gonn1FKqYp+xuB4GeOacZ1Uunk141qtc/pENeJWWnWINLTlBVLJ\nlhqkSmv4eS2ODMc592YXTpISJ9fT58fY3xvUHSKpXlaeom8Gd9dYJzJJaAAOJacXqffRSdavpQj3\n5L5psxYftSAr5U5xll2IUSfvjNBOewbrztGPVGFTOG2NXTq32XP0RWifTitXSl1Jx/Hq7EeSNDm4\nplhfrun8NNJYQgjz2c+LNWh67UEtrquhyGleAzLf52nsWclL/G6lQubNqkGCHMvmejxFz56dWnz3\nU/aK4AIk8uIvM+8+rHlHi8MtyBy3oznLLzUVicR0lX5r2ko/rLAgqaXP6s4f9OPfY1w4jjun0ccN\n/oe0OGySe4BSSjnqkHO8spGVZvOQ8IuGuZWN9zMeTTvYRwKuUKjUauK6e9Yw9j3/zisexXVca3Um\n+1pLDut9xQ3Gu9PGv3uSPWf57QYbZxg6VjCGzx9jTX1WwnXH/umTWmz9nPtsZyT3hMlf46rMXEcf\ndZqRAm13kPDWFrygxS1G9tGdkbj5eudw714Y4D6glFKP5jI+focojPnxlk+0OK0XN/oTis+P64vc\nBtEvgatoc8IEc7jQiz3FUs+95po38nToHNJz3jjzPKKQe/8XQTJTgiAIgiAIbiAPU4IgCIIgCG5w\nX2U+wwrdeVbXSK3G55M2rjHg3EhoIVW43sSlVlUi820IJUWXsI1nw6Er/NAq/gAABktJREFUSCap\nJp00VEza/q0l0p6P+lOgzOFC8vCdxGWSn7v82bPNl/SlVxDpZO8bpFYH95Cidhz+pRYb857X4ugp\n2lnny/lJTb6kKzvnjmrx3t9SuG6sGGnsR0O0edaJm9HYhOTnKT7NRiJN0xVDG4jB9TETTao/bojx\ns9TymU+yTmhx/FYcgoFP4NTLOoY82hRJStrmg9QUkIVckpKN5LPwLq7RyFsUAs3wI82vlFK14eVa\nvLealHmAC+ddiG6+uNKQm/obiCMewQk2H4cUmB7L3PFOREa7Pcf4xceSzg4rRZ6prmSOj+zzvDNT\nKaUi6pBDb/khdZSE0YbEcaQq+zBrOcWG9NJ6Eols7yvINQN1uHTnwpnXto1IpxX/RMp/5ClcPrU2\n5Kn2LObOCp0T1NWsc3AppVJvsobVfgo0mispdHg6B+k5cwFJ61wXfXEgjLPQ2htpQwIKnpqd57cS\njnBGmOtZXkEwW+kjh4s17reC8fcUmcd05+itRp5JbEYiuTGMjOoTtVeLp5MY+5eqGY/rTtZaYwx7\n0cpOpM/ZM4z9eAV70dO+fH76JoVWl3xwY/rvYB50z7AXK6XUqqcp6NlbRYFURw0yTFAA67myDvdb\najxS+1QY69fXi/MeA9uQhJP/kv2i2c4eumoc+X5Wd36q08n8OLoFJ6AnqR9D5r+1Cgm3dZb++0kJ\nZ9ZV1OFsG1L8rXOednrF0wYLdXNV/Aj3sTUj3JfnDKyb2Tykxsjf4/Z9t+x9LQ7MwZn3dL/uDEWl\n1NwQLl9jAnv+Hitny44OsteEpOG289mA5O9cYL8MtdP3nWcZt3dLWbP/2MF+MWBiXiy8iLv43dvI\nnPuPce9Wr/G7/xeSmRIEQRAEQXADeZgSBEEQBEFwg/sq83UMkw4f2Yt7w/EWecY34pC8zkSRrjS5\nSFHe3YMEdOIoqbiNfaSxc1q/pMX1Y6SciwpxHuUGkKJNXYf0UHuXlGZWC+nKqgQK7ymlVGI+To6u\nT0g5B2bw75/5IIeEJVAosrsVGWdsAynkyPqntNj/Mqnv57KRni7acMNNpCGTmbOQ25LHKEo3MEWa\n3FMs3CRNGjJMKrmwBEmlrxfJIDGRNKxrK/JPahNt7OxlXJMmKXB6tRR51Pckn7lmJc37VBzSZxeq\nkGrMQIrd4I1UcSee71dKqSwb8ozFipTYpTt37/Yt2vA6NT5V6wK/MdyHrBIXQUHKwXBk7aYIxnjd\nOA6W/gtcn/ccTpVVKBvK8nvmplreBLcYNCFvJI/pJNA0xjB8A+0/28+4RQwzN9e8SmFAu0Iuix2i\nYK8lEKk9pRsH7hUzbd4QQPHAzlbmV0UihTCjr/L9Y3++XOYb/xAZo+QYn+t+jc4s/OAhLU64R3q/\ncCMFOc+lIKus9qWPvNqQBWuzf6LFwRnIHnNXkEkiE9lf0sZYO+OP4iL2FDF+SKHT87QrsYcxiN/A\n/jhs/xstdhxE/vqE7VE5EpF7N48hTfbcxM1l0BUonupiL7aUMYc+KWPNfrmBvXg6Gil/JgH5VSml\nmq5z3WE7mF+x/UhBpz5gjCvW0H5rE40YXuT+szTBWW4rotFsx9+iv5oSkYh813C+XEEdctGdXPri\nmYPIouo15THmSijgu9XCtfrFcU/MDmGuWVfQ33YXbmSvMN0rAt7cW+eCeZXB1sP+EqErWD2Ux/4Y\nTRepnCJeqVi1yH02IgGnYdAjy2Vb7z7WQncw8ndGPp8bTOCeZR9jXoUtImH365zsu51I1VGvsHf0\nGZi3h/t0BZ+3c19+vZX923f+Cb7/VeTML4JkpgRBEARBENxAHqYEQRAEQRDcwLC0tPSHPyUIgiAI\ngiD8r0hmShAEQRAEwQ3kYUoQBEEQBMEN5GFKEARBEATBDeRhShAEQRAEwQ3kYUoQBEEQBMEN5GFK\nEARBEATBDeRhShAEQRAEwQ3kYUoQBEEQBMEN5GFKEARBEATBDeRhShAEQRAEwQ3kYUoQBEEQBMEN\n5GFKEARBEATBDeRhShAEQRAEwQ3kYUoQBEEQBMEN5GFKEARBEATBDeRhShAEQRAEwQ3kYUoQBEEQ\nBMEN5GFKEARBEATBDeRhShAEQRAEwQ3kYUoQBEEQBMEN5GFKEARBEATBDeRhShAEQRAEwQ3kYUoQ\nBEEQBMEN/htNfoFEIet43gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff0b8098ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "W = best_svm.W[:-1, :]    # delete the bias\n",
    "W = W.reshape(32, 32, 3, 10)\n",
    "W_min, W_max = np.min(W), np.max(W)\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i+1)\n",
    "    imgW = 255.0 * ((W[:, :, :, i].squeeze() - W_min) / (W_max - W_min))\n",
    "    plt.imshow(imgW.astype('uint8'))\n",
    "    plt.axis('off')\n",
    "    plt.title(classes[i])\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
